A Framework for Active Sensing and Processing of Complex ... - MDPI

sustainability Article

ASPIE: A Framework for Active Sensing and Processing of Complex Events in the Internet of Manufacturing Things Shaobo Li 1,2 , Weixing Chen 1 , Jie Hu 1,3 and Jianjun Hu 2,3, * 1

2 3

*

ID

Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang 550025, China; [email protected] (S.L.); [email protected] (W.C.); [email protected] (J.H.) School of Mechanical Engineering, Guizhou University, Guiyang 550025, China Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA Correspondence: [email protected]

Received: 2 February 2018; Accepted: 1 March 2018; Published: 4 March 2018

Abstract: Rapid perception and processing of critical monitoring events are essential to ensure healthy operation of Internet of Manufacturing Things (IoMT)-based manufacturing processes. In this paper, we proposed a framework (active sensing and processing architecture (ASPIE)) for active sensing and processing of critical events in IoMT-based manufacturing based on the characteristics of IoMT architecture as well as its perception model. A relation model of complex events in manufacturing processes, together with related operators and unified XML-based semantic definitions, are developed to effectively process the complex event big data. A template based processing method for complex events is further introduced to conduct complex event matching using the Apriori frequent item mining algorithm. To evaluate the proposed models and methods, we developed a software platform based on ASPIE for a local chili sauce manufacturing company, which demonstrated the feasibility and effectiveness of the proposed methods for active perception and processing of complex events in IoMT-based manufacturing. Keywords: Internet of Manufacturing Things (IoMT); critical event; active perception; complex event processing; big data; real-time event

1. Introduction With the interpenetration of information technology and manufacturing, the integration of manufacturing physical systems and information systems has been increasingly enhanced [1]. The IoMT technology with embedded systems, RFID, and wireless sensor networks at its core, has come into shape as a ubiquitously-perceptual, heterogeneously-integrated, self-adaptive, interoperable, and intelligent manufacturing and information service model. The application of Internet of Things (IoT) technology in the manufacturing industry provides the technical basis for interconnection, real-time position tracking, intelligent control, and so on in the manufacturing process [2]. It also promotes the fusion of manufacturing physical systems and information systems, and can achieve dynamic perception and optimized processing of resources and information production. IoT has been utilized in decision-making in manufacturing to contribute to knowledge-based intelligent manufacturing [3,4]. With the development of more intelligent manufacturing processes, an increasing number of studies have been conducted to explore techniques for manufacturing information perception. Since 2003, the emergence of RFID and other IoT technologies have brought an intelligent revolution in manufacturing [5], which has helped to achieve automated data collection [6], assurance of data Sustainability 2018, 10, 692; doi:10.3390/su10030692

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dependencies, improvements in production and inventory visibility [7], etc. Kenneth et al. [8] proposed a Radio Frequency Identification (RFID) -based risk management system (RRMS) with rule-based reasoning and RFID integration technology to more effectively monitor real-time operations on site. Barenji et al. [9] proposed a multi-agent RFID-enabled distributed control and monitoring system for a flexible manufacturing shop to enhance the agility, flexibility, and reconfigurability of its manufacturing system. Brintup [6] proposed to use RFID technology to achieve leaner manufacturing through automated data collection and improvements in better monitoring of production and inventory. Liukkonen [10] showed that most application targets of RFID are related to warehouse management, process management, tool management, supply chain management, and life cycle management. Wireless sensor networks [11] have been used for the perception of mobile units to achieve anticipatory control for a flexible manufacturing system. Zhang et al. [12] developed a real-time information capturing and integration framework of the Internet of Manufacturing Things based on Internet of Things (IoT) technology. Tao et al. [13] proposed a framework to use IoT for achieving intelligent perception and access of various resource in cloud manufacturing. Jeschke et al. [14] and Tao et al. [15] introduced frameworks for industrial IoTs and cyber-manufacturing systems. It is widely recognized that comprehensive perception and intelligent processing of multi-source manufacturing information are critical to decision support for intelligent controls in production processes [16]. The decreasing cost of sensors and the need for intelligent manufacturing has led to the adoption of diverse types of sensors in modern factories for quality control, fault diagnosis, etc. However, this has caused significant challenges in big data processing of such enormous amounts of heterogeneous information from such sensors: how to detect different types of events; how to match events to appropriate decision-making rules; and how to handle the real-time requirement of complex events processing. Among the datasets produced in the process of interconnected manufacturing, the manufacturing events that guide decision support include primitive events and critical events [17]. Primitive events occur when the perception components of equipment collect raw data. Critical events are complex decision-making events that are generated by association rules from primitive events or complex events [18]. The manufacturing events of a manufacturing process can be complicated due to the distributed deployment structure of the manufacturing system. A complex event processing engine takes the massive heterogeneous data as the observation and extraction objects, obtains meaningful event information from regular operations, and makes up the complex events so that meaningful events can be extracted and summarized in real-time [19]. These concurrent event processes have been discussed by Tanase et al. [20]. The main focus of this paper is how to deal with the complex relationships among multi-level manufacturing events to achieve fast and intelligent complex event processing. Several pioneering studies have been done in this area. Vaidehi et al. [21] designed an ECA language for wireless sensor networks to generate data and events. A single ECA was used to describe simple events and ECA combinations were used to represent complex events. Gerald et al. [22] developed the Cordis event processing model by adopting complex event processing in the event-related research of distributed systems. They also suggested an algebra to describe the association between event information and an algorithm to detect the independent event information in the association description. Romdhane et al. [23] presented a unified processing framework for uncertain information based on probability theory in the process of complex event recognition and created a new composite event model description language to overcome the “complex temporal constraints”. Wang et al. [24,25] developed a distributed complex event processing architecture based on the Common Object Request Broker architecture. They also designed an algorithm of plan-based distributed CEP (PDCEP) and a high-performance distributed complex event processing method based on probability, extending the basic finite automata to support the probability of event flow processing models. Yao et al. [26] defined RFID event modeling and complex event operators and presented an architecture for complex event processing for real-time processing of RFID events and the integrity of logical semantic description.


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Hu et al. [16] showed that event construction, state prediction, and disturbance detection suitable for big-data environments of modern complex manufacturing systems are critical for RFID-enabled intelligent manufacturing. It is also noted that the greatest challenge is to design efficient and intelligent schemes for manufacturing event processing. Allen et al. [27] proposed an anomaly detection approach for event-based systems that consist of processes that interact through shared resources. A resource-based Petri net formalism is introduced to model these types of systems. Their model generation uses an algorithm based on workflow mining to generate resource-based models. Dias [28] studied how to use timed event graphs to simulate synchronizing operations on productive systems. Complex event processing in IoMTs can also exploit the emerging big data technologies and platforms, such as Apache Spark [29], real-time stream processing platforms [30,31], etc., [32–34]. The complex events in IoT-based manufacturing processes can be regarded as flows of information from multiple sources, for which Cugola and Margara [35] conducted a comprehensive review considering issues from data streams to complex event processing. Fang et al. [36] introduced an information processing mechanism based on a critical event model to organize real-time field data in various abstract levels for enterprise decisions. Their case study at an air conditioner manufacturing company showed its effectiveness. Fault diagnosis of discrete event systems has also been analyzed by Zaytoon et al. [37]. Event-based models have also been used for modeling distributed sensor networks in battery manufacturing [38]. Due to the ubiquity of RFID-based manufacturing plants, new computing methods are needed for rapid processing of the big event data from hundreds or thousands of sensors. Dagkaris et al. [39] proposed and shared ManPy, an open-source software tool for building discrete event simulation models of manufacturing systems. Nie et al. [40] adopted an event handling engine (PUCEP) to improve uncertain complex event matching efficiency by combining the data stream processing engine (SASE) with data generation management and the probability flow theory. The purpose is to link the Internet of Things to data collection and improve the accuracy and reliability of complex events matching. Li et al. [41] suggested a spatiotemporal event model for the complex event processing of farmland IOT, and used the complex event relation operator to judge the spatial-temporal relationship between sub-event and complex event. They also designed and implemented a real-time complex event processing engine, to determine complex events through real-time matching of atomic events and complex event patterns. However, current complex event processing for manufacturing processes lacks a standard model and mechanisms for semantic expression of complex association relations among events. It becomes an urgent need to improve complex event matching processing with strong association rules. To achieve active sensing and processing of key events in manufacturing processes, this paper proposes an active sensing and processing architecture (ASPIE) for processing manufacturing process events. First, an active sensing method is developed for obtaining key events in manufacturing processes. To address the issue of event expression and processing, an XML-based extensible and easy-to-parse event description language with related event operators is designed to realize the unified description of complex semantic relations. We also proposed a sophisticated event matching scheme to conduct complex event processing based on correlation templates. To evaluate our active sensing and processing architecture and methods, we implemented an ASPIE module embedded in the manufacturing resources management system at a large factory famous for chili sauce production, aiming to support intelligent decisions and optimization of their manufacturing system. The framework and methods for handling the perception and processing of key events in manufacturing processes may provide theoretical and practical guidance for developing enterprise production management decision-making systems. The remaining paper is organized as follows: In Section 2, an architecture for active sensing and processing of key events in IoMT is introduced. In Section 3, we proposed the methodology for active sensing of key events in manufacturing processes including system design, data processing and packaging, complex events association mining and matching, and transmission and access of the data obtained via active sensing. Section 4 describes how to describe and process the manufacturing


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events in a standardized way using an improved XML language and template matching. In Section 5, we described our implementation and evaluation of the proposed architecture, models and methods for active sensing and processing of key events at a chili oil sauce manufacturing company. In Section 6, the concluding remarks are presented, and the contributions of this paper and directions for future Sustainability 2018, 10, x FOR PEER REVIEW 4 of 21 research are discussed. the concluding remarks are presented, and the contributions of this paper and directions for future

2. Architecture ofare Active Sensing and Processing of Key Events of IoMT research discussed. 2. Architecture of ActiveofSensing and Processing of Key Events of IoMT 2.1. Architecture of the Internet Manufacturing Things

The Internet of Manufacturing Things is Things an open network system that integrates advanced 2.1. Architecture of the Internet of Manufacturing manufacturing, Internet of Things, information, It serves all stages of The Internet of Manufacturing Things is anand openmodern network management. system that integrates advanced the product manufacturing service process and andmodern the whole manufacturing cycle.ofBased on the manufacturing, Internetand of Things, information, management. It serves all stages the product and technology service processand andthe the expectation whole manufacturing cycle. Based on characteristics of manufacturing manufacturing of the objectives, wethe proposed a characteristics of manufacturing technology and the expectation of the objectives, we proposed a framework of hierarchical manufacturing object networking with frame supporting function (Figure 1). framework of hierarchical manufacturing object networking with frame supporting function (Figure 1). Decision making Environmental Monitoring

Equipment Maintenance

Process Coordination

Energy Conservation

Error Alarm

Production Scheduling

... Support Technology

Application support

Language and Tools

Operating Environment

Middleware

...

Application Layer Identification Parsing

Sensor Network

Mobile Communication Network

Internet

Industrial LAN

...

Trasport Layer Network Management

Networking and Information Processing Network Selforganization Technology

Information Cooperative Processing Technology

Sensor Middleware Technology

Short - distance Transmission Technology

... Information Technology

data collection

Sensor

PLC System

Barcode Scanning

RFID

Testing Equipment

DCS Equipment

...

...

Perception Execution Layer

Figure 1. Manufacturing networking architecture composed of perception layer, transport layer, and

Figure 1. application Manufacturing networking architecture composed of perception layer, transport layer, layer. and application layer. The whole architecture includes the perception execution layer, the transport layer, and the application layer according to the application pattern and development trend of the Internet of The whole architecture includes thelayer perception execution the transportprocess layer, and the Things in manufacturing. The sensing executes the perceptionlayer, of the manufacturing resources, environmental and decisions afterand receiving feedback. The transport layer application layer according toinformation, the application pattern development trend of the Internet of implements data and network transmission, a real-time transmission service process Things in mainly manufacturing. Theaccess sensing layer executes theprovides perception of the manufacturing for the production scene examination data, and issues the management level monitoring instructions. resources,The environmental information, and decisions after receiving feedback. The transport layer data transmission layer defines the network identification and communication protocol so that

mainly implements data access and network transmission, provides a real-time transmission service for the production scene examination data, and issues the management level monitoring instructions. The data transmission layer defines the network identification and communication protocol so that


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the heterogeneous network data can be recognized and merged with each other to gain access to Sustainability 2018, 10, x FOR PEER REVIEW 5 of 21 the heterogeneous network. The application layer provides support for decision-making application servicesthe with the information heterogeneous networkprocessing data can be tools. recognized and merged with each other to gain access to the heterogeneous network. The application layer provides support for decision-making application

2.2. Active Process Model for Manufacturing Processes services withPerception the information processing tools. Active sensing IoMTModel events in the manufacturing processes involves five stages: 2.2. Active Process of Perception for Manufacturing Processes (1) multi-source heterogeneous manufacturing data acquisition and transmission; (2) data isomorphism, Active sensing of IoMT events in the manufacturing processes involves five stages: (1) multistandardized description of the production process events; multi-level event source processing, heterogeneousa unified manufacturing data acquisition and transmission; (2) data (3) isomorphism, association modeling, and multi-level event relationship analysis; (4) multi-level event correlation and standardized processing, a unified description of the production process events; (3) multi-level event operation of the match operations, obtaining the perceived results of key events; (5) push and association modeling, and multi-level event relationship analysis; (4) multi-level event and correlation and operation results of the match access of perceived [42]. operations, obtaining the perceived results of key events; and (5) push and access of perceived results [42].manufacturing process event is designed as shown in Figure 2, An active sensing model of the An active sensing model of the manufacturing process event is designed as shown in Figure 2, for the active sensing stage of the manufacturing event. for the active sensing stage of the manufacturing event.

Data Collection

Event Data Correlatio Processi n Model is ng Establish ed

Analysis of Multi-Layer Event Correlation

Event Matchin g and Operatio n

Multi-Source Real-Time Manufacturing Information

Data Storage Center

Data Transmission

Active Push and Remote Access

Figure 2. The sensing active sensing model of events. IoMT events. It is composed data collection, data Figure 2. The active model of IoMT It is composed of dataofcollection, data transmission, transmission, data processing units. and data processingand units.

2.3. Architecture of Active Sensing for Complex Events in IoMT

2.3. Architecture of Active Sensing for Complex Events in IoMT In this paper, we propose the following active sensing architecture for manufacturing events

In (Figure this paper, weincludes propose the following active sensingdata architecture for manufacturing 3), which data acquisition, data transmission, characterization, data integration, events application, and data [43]. (Figure data 3), which includes dataservice acquisition, data transmission, data characterization, data integration, The sensing layerservice (Figure [43]. 3) is a highly-reliable manufacturing data-aware environment. The data application, and data main task i : (1) to build a sensing environment for data acquisition and transmission, to establish a The sensing layer (Figure 3) is a highly-reliable manufacturing data-aware environment. The main sensor network configuration model for measuring different parameters, and to determine the target task i : (1) a sensing environment for data acquisition transmission, establish a sensor of to thebuild sensor network function and constraints; (2) and to establish sensorto information network configuration modelprocessing for measuring different parameters, andtoto describe determine thedefine target of the registration/information library and data description, and sensor network function andinformation constraints; (2)various to establish sensor information registration/information manufacturing resource with sensors; and (3) the perception and transmission of real-time manufacturing resources, which isand manifested the manufacturing resources processing librarydata andofdata description, to describe define when manufacturing resource information reach the sensing area. At that time, the data is automatically collected or identified by the with various sensors; and (3) the perception and transmission of real-time data of manufacturing equipments that are aware of manufacturing resource information. Then, the event is perceived by resources, which is manifested when the manufacturing resources reach the sensing area. At that time, the sensor in real-time. Through the network communication protocol, the sensor device will be able the datatoiscollect automatically collected or identified by data the equipments that are aware ofthe manufacturing data of the manufacturing resources. The acquisition layer is used to collect multiresource information. Then, theand event is perceived by the sensor real-time. Through theisnetwork source manufacturing data manage the heterogeneous sensor. in The data transmission layer employed protocol, to integratethe thesensor heterogeneous and provide thedata interoperability to meet the very communication device networks will be able to collect of the manufacturing resources. large volume of real-time sensing data transmission needs. As the heterogeneous network in the The data acquisition layer is used to collect the multi- source manufacturing data and manage the manufacturing of things uses different protocols, the data transmission layer ensures that heterogeneous sensor. The data transmission layer is employed to integrate the heterogeneous transmission of heterogeneous network data to be real-time and transparent. The data networks and provide the interoperability to meet the very large volume of real-time sensing data characterization layer is for sensor equipment registration, data definition, and its standardization. transmission needs. As the heterogeneous network in the manufacturing of things uses different protocols, the data transmission layer ensures that transmission of heterogeneous network data to be real-time and transparent. The data characterization layer is for sensor equipment registration, data definition, and its standardization.


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Sustainability 2018, 10, x FOR PEER REVIEW

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Manufacturing Execution System

Applications

Visualization Factory

Equipment maintenance

Lean Production

...

Energy Conservation Data Center

Application Service Layer Quality realtime monitoring and diagnosis

Manufacturing Process Monitoring / Collaboration

Monitor Events

Enterprise Resource Management Information System

Data Integratio n

ERP

CRM

...

WMS

Manufacturing Resources Real Time Monitoring

Material Optimization Distribution

Production Task Scheduling

Manufacturing Execution System MES

CAPP

...

QMS

Process Monitoring

Basic Control Data PLC

DCS

...

DNC

Manufacturing Knowledge / Resources

Unified Data Integration Interface

Information Processing Layer

Multi-source Manufacturing Information Relationship Definition

Data Integration

Multi-source Data Entity Identification and Cleaning

Key Events and Multi-Level Event Correlation Operations

Key Event Data Increment Processing

Event Information is Standardized Encapsulation

Manufacturing Information Processing and Extraction Rules

Event Percepti on and Process ing

Data Characterizatio n

Sensing Layer

Data Transmission

Sensing equipment information registration

Sensing device data acquisition

Data classification and coding

Data XML Standardization

Data definition

Sensor Network Optimization ConfigurationRule s

Internet | Industrial LAN | Wireless | RF | Bluetooth | Infrared

...

Data Collection RFID Devices Sensing Equipment

Production Equipment

PLC Devices

DCS Equipment

Testing Equipment

Sensor Information Registration / Processing Library

HumanComputer Interaction

Multi - source Information Aggregation Rules

Figure 3. Architecture active sensingand and processing processing of It isItcomposed of three layers:layers: Figure 3. Architecture of of active sensing ofIoMT IoMTevents. events. is composed of three the sensing layer, information processinglayer, layer, and and the layer. the sensing layer, the the information processing theapplication applicationservice service layer.

The information processing layer (Figure 3) is employed to receive the data sent by the object

The information processing layer (Figuredata 3) isintegration employed to receive the data sent by the object perception layer. According to the established rules, the perception data is processed perception themapping established rules, the (such perception data isoutput processed in real layer. time. ItAccording establishesto data and data eventintegration handling mechanism as standard in real time. It establishes dataevent mapping and event handling as standard data model formulation, correlation operation and mechanism value-added (such processing rules). output It also data implements dataevent processing and information integration, and obtains key events of model formulation, correlation operation and value-added processing rules). information It also implements in the production process. The integration layer isofused for data monitoring processingand anddecision-making information integration, and obtains key data events information monitoring relationship definition, correlation operation, and The value-added processinglayer in theisproduction process and decision-making in the production process. data integration used for relationship of multi-source information. The data integration layer performs the collection of production-aware definition, correlation operation, and value-added processing in the production process of multi-source events and the classification of storage to support efficient data transfer. information. The data integration layer performs the collection of production-aware events and the In order to meet the requirements of real-time monitoring in the manufacturing process, classification of storage to support efficient data transfer. production plan execution, equipment condition detection, material optimization and distribution, In order to meet requirements of real-time monitoring manufacturing process, and product qualitythe inspection, the application service layer callsinorthe accesses the perceptual production plan execution, equipment condition detection, material optimization and distribution, information stored in different systems of manufacturing enterprises, and is used to optimize and product quality inspection, thecenter application layerinformation calls or accesses information production decisions. The data providesservice the service neededthe forperceptual the manufacturing process management system, such as sensorenterprises, network optimization configuration multi-source stored in different systems of manufacturing and is used to optimizerules, production decisions. information aggregation sensor information registration database, manufacturing information The data center provides therules, service information needed for the manufacturing process management processing extraction rules, and enterprise manufacturing resource knowledge bases. aggregation system, such as and sensor network optimization configuration rules, multi-source information rules, sensor information registration database, manufacturing information processing and extraction 3. Active Sensing Method for Key Events of Manufacturing Processes Based on IoMT rules, and enterprise manufacturing resource knowledge bases. In order to realize active sensing and standard output of the shop manufacturing process, the multi-hop heterogeneous sensor be adopted in the Processes manufacturing workshop to manage 3. Active Sensing Method for Keynetwork Events can of Manufacturing Based on IoMT

In order to realize active sensing and standard output of the shop manufacturing process, the multi-hop heterogeneous sensor network can be adopted in the manufacturing workshop to


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manage the heterogeneous sensors and to collect the dynamic data. In data processing, firstly, Sustainability 2018, 10, x is FOR PEER REVIEW 7 of 21 the sensor information defined according to the sensor type and data description template, and the heterogeneous data is normalized by the ISA95 standard encapsulation data [44]. Secondly, based on the heterogeneous sensors and to collect the dynamic data. In data processing, firstly, the sensor the event business logic and rule based algorithms, key events are obtained by matching the perceptual information is defined according to the sensor type and data description template, and the data based on the XML Finally, the standard perceived key eventsdata are[44]. transmitted through heterogeneous data istemplates. normalized by the ISA95 encapsulation Secondly, based on Web servicethe technology. event business logic and rule based algorithms, key events are obtained by matching the perceptual data basedsteps on theofXML templates. the perceivedprocess key events are based transmitted The implementation active sensing Finally, of manufacturing events on the IoT through Web service technology. technology are as follows: The implementation steps of active sensing of manufacturing process events based on the IoT

(1) (2) (3) (4)

The design are of the perception system; technology as follows: Data processing standardized packaging; (1) The design ofand the perception system; Complex correlation matching; and (2) Data event processing and standardized packaging; (3) Complex event correlation matching; Transmission and access of perception and results. (4) Transmission and access of perception results.

The process of active sensing and processing manufacturing complex events is shown in Figure 4. The process of active sensing and processing manufacturing complex events is shown in Figure 4. The details of theofprocesses areare explained subsections. The details the processes explainedasasbelow below in in subsections. system Integration terminal

Unified User Interface

Intermediate Member Perceptual Node Module Base Member

Raw Perceptual Data

Data Analysis

Industrial Computer

Perceptual Data

Unified Description of Sensor Data

No Processing ？ Yes

Multi-source Heterogeneous Sensor

Heterogeneous Interface Integrator

Manufacturing Resource Register

Perceptual System Design Employee/ Material Information

Quality in the System

Equipment Conditions

···

Rule Operation

Production Plan Execution

Manufacturing Information Critical event push and access modules Perceptual System

Matching Operation and Encapsulation Module of Multi-level Events Multi-source Heterogeneous Information Processing and Standardization Module

XML

Equipment Condition Monitoring Module

Standardization and Packaging

Manufacturing Information for Standard Structures

Sensor Plug-and-Play Operation Module

Data Processing and Packaging Manufacturing Resources Standardized Data

Perceptual System Integration Module

Analysis of Multi-level Event Correlation Logic The Event Correlation Model of RealTime Response is Established Analyze the Event Correlation Policy A Matching Association Scheme based on Data Mining Algorithm is Established

Analysis of Event Flow and Event Handling of Ideas

Key Event Characteristics and Event Template Composition Characteristics

Improve the Classic Data Mining Algorithms A Data Matching Operation Based on Statistics and Generating a Matching Template

Improved Algorithmic Performance in Esper

Template - Based Event Processing for Production Processes

Critical Event Perceived Results Data Association Matching Processing

Figure 4. Architecture ofofactive andprocessing processing of IoMT events. Figure 4. Architecture activesensing sensing and of IoMT events.


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3.1. Perception System Design Due to the sensor heterogeneity, an embedded manufacturing information sensing device is designed to conduct multi-source heterogeneous data acquisition, network I/O control, rule based algorithm processing, and data storage and transmission. It also realizes heterogeneous sensor concentration management and heterogeneous information-aware processing. In the perception system, real-time manufacturing information can be acquired with the underlying components of the sensing node module. The intermediate component integrates the data collected by the bottom node, real-time transmission, and decision briefing, and generates events through the logical operation of data information. A unified user interface facilitates the development of the system and the efficient transfer of information. A unified description of the sensor information achieves the standardization of information processing and expression. For the sake of generality, real-time sensing data per sensor is encapsulated as a standard Web service so that real-time data can be transmitted over the network. Perception system integration of functional modules mainly includes plug-and-play of heterogeneous sensors, condition monitoring equipment, multi-source heterogeneous information processing and standardization, multi-level event matching operations and packaging, and event push and access. 3.2. Processing and Standardized Package of Perception Data According to the data structure of storage, the real-time manufacturing information is divided into dynamic data and static data. Dynamic data is stored as XML documents, of which the data structure is semi-structured and unstructured. Static data (basic data) is the property data of manufacturing resources, also referred to as the basic information of the object, which is stored in a relational database. In order to achieve the unified expression and efficient call of heterogeneous data, the relational data and XML data can be exchanged, and the perception data is encapsulated and stored as XML documents. 3.3. Correlation Matching of Complex Events First of all, we need to integrate the manufacturing information that’s processed and packaged in a standard package (including multi-source manufacturing information related to definition of association, template matching of multi-level events, regular operation, and increment processing of events). The event association matching process consists of three steps: (1) to establish an event association model using XML language to describe the association of multi-source manufacturing information, and to establish an XML file describing the event structure; (2) to mine association rules of events based on association data mining algorithms and to establish the correlation matching templates of complex events; and (3) to improve the performance of the algorithm in the event stream processing engine and perform the calculation of the complicated events in the production process based on template matching. 3.4. The Transmission and Access of Perception Results Perceived complex events are mainly based on Web service technology for active push and remote access, as shown in Figures 5 and 6.

based on template matching. 3.4. The Transmission and Access of Perception Results Perceived complex events are mainly based on Web service technology for active push9 of and 21 remote access, as shown in Figures 5 and 6.


Rule Operation

WiFi


TCP/IP

Production Information Management System

trigger

Original Information

Active Push

Rule Operation

Percei ved Key Event s

PDA Application System Warehouse Management System Enterprise Resource Planning

Registration / Binding Push Address

Multi-level Events Rule Operation

······

Sustainability 2018, 10, x FOR PEER REVIEW Figure 5. Active push service of key events.

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Figure 5. Active push service of key events. transfer

Rule Operation

Remote Access


Original Information

Production Information Management System

Rule operation

Registration / Binding

PDA Application System

Perceiv ed key events

Address

Warehouse Management System Enterprise Resource Planning

Multi-level events

WiFi Rule operation

······

TCP/IP

Figure 6. Remote access service of key events.

4. Standardized Descriptions and Processing Of Manufacturing Process Events With the improved XML language, the standardized processing of manufacturing events is achieved in order to realize the unified semantic expression of the complex relations of multi-level events in the manufacturing process. At the same time, we developed developed a complicated complicated event association based on on templates. templates. matching scheme to process complex events based 4.1. Correlation Model Model of of Manufacturing Manufacturing Association 4.1. Correlation Association Events Events In the relationship between the the manufacturing process events events are: are: (1) In general, general, the relationship between manufacturing process (1) temporal temporal relationships or time-sequence relationships, which can be described using time models; relationships or time-sequence relationships, which can be described using time models; (2) (2) logical logical hierarchies, be described described using using hierarchical hierarchical models; models; and and (3) (3) causal causal relationships, relationships, described described hierarchies, which which can can be using semantic operators operatorsfor forexpression. expression.Assuming Assuming represents a key event or complex event. using semantic CECE represents a key event or complex event. PE PE represents an original event. expression event be written = f(PE1, PE2, represents an original event. TheThe expression for for thethe keykey event cancan be written as: as: CECE = f(PE1, PE2, ···, ···, CE1, CE2, ···). logical The logical operator f defines the logical relationship between can be CE1, CE2, ···). The operator f defines the logical relationship between eventsevents and canand be nested nested in multiple in multiple layers. layers. Based on the production process event type and correlation analysis, a complex event structure model oriented 7).7). Relational operators areare used to oriented to to the theproduction productionprocess processisisestablished established(see (seeFigure Figure Relational operators used describe the relationship between the multi-level events, such as timing and logic hierarchy, to reveal to describe the relationship between the multi-level events, such as timing and logic hierarchy, to the information of sub-events and theirand implications in multi-level complex events, andevents, to explain reveal the information of sub-events their implications in multi-level complex andthe to selection and matching of sub-event instances of complex events. The temporal and semantic relations explain the selection and matching of sub-event instances of complex events. The temporal and between events can also be identified. semanticmulti-level relations between multi-level events can also be identified. CE2

CE1

CEn

TRUE TRUE CE11

···

CE1n

non

FALSE [T1，T2]

Time Constraints

PEm

PEn

Based on the production process event type and correlation analysis, a complex event structure model oriented to the production process is established (see Figure 7). Relational operators are used to describe the relationship between the multi-level events, such as timing and logic hierarchy, to reveal the information of sub-events and their implications in multi-level complex events, and to explain the selection and Sustainability 2018, 10, 692 and matching of sub-event instances of complex events. The temporal 10 of 21 semantic relations between multi-level events can also be identified. CE2

CE1

CEn

TRUE TRUE CE11

···

CE1n

non

PEn

FALSE Time Constraints

[T1，T2]

PEm

among them:

order

CE

Critical Events

PE

Primitive Event

TRUE

非

TRUE

PEm

PEn

FALSE

Event Operator [T1，T2]

Time Zone Constraints

Figure 7. Structure model of complex events in IoMT. Figure 7. Structure model of complex events in IoMT.

There exists an association of similar event type aggregations between event CE1 and event CEn. For event PE, the CE1n upper level event is CE1, and there is a generalized relationship between event CE1, events CE1n and PE. The aggregation process for the critical event CE11 [45] is: (1) if the original event PEn occurs, the event value is TRUE; (2) if the original event PEm does not occur, the event value is FALSE, and the non-operator aggregation event value becomes TRUE; (3) after the two original events are processed, the event value becomes TRUE by the sequence operator; and (4) after the time constraint operator is aggregated, the event value becomes TRUE, i.e., the event CE11 occurred. 4.2. Description of XEDL-based Manufacturing Events (1)

Basic Syntax Structure

Our basic syntax definition of XML-based event description language (XEDL) includes three aspects: (1) XEDL text should conform to the XML specification, the starting line should be: ; (2) With XEDL as root: . . . /XEDL>; (3) Under the root, there are three parts: event structure, event strategy and event attributes. The XEDL syntax consists of a collection of raw event registries and key event sachems. The syntax is described as follows: //Primitive event registry . . . //Primitive event description //A collection of key event patterns . . . //Key event patterns

(2)

Description Syntax of Original Events

The primitive event triggered by the sensor node is represented by a unique identifier ID and it represents a change in the same class or similar state. The original event body contains the source sensing device, the time, the location of occurrence, and other attributes. Description syntax of the primitive event is as follows:


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primitive event_i//The original event i uniquely identifies the ID //The original event occurs from the source sensor node sensor_n//Sensing source n //Other attribute definitions otherattr_m//The attribute m

(3)

Description Syntax of Key Events

A key event is described as a complex event pattern, which includes three aspects: the cause event set, the instance strategy, and the logic operation function. The key event expression consists of the event operator and the operand, denoted as CE expression (operator, operand). The operands represent the event type, and the operators combine the event types. The definition of event operator [46,47] consists of three parts: (1) the number of instances of the operands; (2) the usage strategy and consumption strategy of the event instance; (3) the usage strategy of event instances consists of four operators: primitive, recent, continuous, and cumulative; and the consumption strategy of the event instance consists of four operators: reserve, remove-early, remove-current, remove-all. Qualified events are reserved following the usage strategy or consumption strategy of the operand processing or after deleting the ineligible instances when the number of event instances of the operand is exceeded. The usage strategy of an event instance specifies how an event instance can be used for event aggregation. The consumption policy of the event instance determines whether the complex event is saved or deleted after it is involved in complex event calculations. Combined with the complex event operators and XML syntax, the key event XEDL syntax is defined as follows: complex_event_n// The unique ID of the key event n •••// Definition of event attributes // An set of cause event instances •••// Acause event instance and strategies // The logical operation function of the event conjunction|disjunction//Judgmentfunction •••// calculation function of attributes

(1)

The Policy Description of the Causal Event Instance

The use of the event instance, the consumption policy, and the cause event are described in XEDL syntax as follows: // Use policy definitions primitive|recent|continuous|cumulative// Use the policy options // Definition of consumption policy←← reserve|removecurren|removeearly|removeall// Consumption policy options


(2)

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Syntax Description of the Logic Operation Function

// Event logic operation function // Judgment function position|source|time|other// The type of the event attribute ••• // The calculation function for the attribute •••// The event property name is evaluated •••// The calculation of the variables •••

4.3. Manufacturing Process Event Processing Based on Complex Event Processing (CEP) and Associated Templates The manufacturing process event is processed by matching relation scheme based on template sequences. Firstly, the Apriori algorithm is used to mine the event data set and generate the matching template, which is stored in the memory database in XML format. Then, the template-based complex event processing operation is performed, and the key events of the manufacturing process are obtained [17]. (1)

Manufacturing Process Event Matching Association Scheme

The Apriori algorithm is used to mine association rules and generates matching templates. The algorithm rules are: (1) if there is a causal relationship between active-perceived events and sub-events, the confidence level of the association rules is 1; (2) there are one-to-one, many-to-one association rules between perceived complex events and the causal events. There is no need to consider one-to-many or many-to-many cases; and (3) for the one-to-one association rules of events, there is a sufficient non-essential conditional relation between the source event and the target event. The algorithm of matching template [48] is generated as follows: 1: begin 2: U ← (History-aware event patterns from the database); 3: for all Ei∈U do 4: Di.id = newid;//Complex Event Matching Pattern Detection 5: for all Di∈U do 6: if (Di = Φ) 7: Delete (Di); 8: if (Di - 1! = Φ) 9: Di-2 = Di-2∪Di-1;//The last level of event handling 10: Al ← {i∈Di, and i satisfies the term of the support degree S }; //A 1-level association rules with minimum support 11: for k = [2,3, . . . ] do 12: Tk = Ak -1 × Al; //Multilayer transaction matching 13: Ak {i∈Tk and i satisfies the term of the support degree S}; //A k-level association rule with minimum support 14: if (Ak = Φ) 15: break; 16: E ← (The event group in Ak (k! = 1) conf(e) = 1); 17: Association template ← {the event group e∈E, conf(e) = 1}; 18: end


(2)

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Template-Based Matching of Production Process Events

To achieve the complex processing and analysis of multiple atomic events in real-time, we chose template matching as the complex event processing data stream processing scheme. The template is stored in the knowledge base of “in-memory database”, and the production process events are handled by template matching complex Sustainability 2018, 10, x FORand PEER REVIEW event processing. The main flow is shown in Figure 8. 13 of 21

Event Information

Perceptual data

Data matching

Y

N Data matching

matching template

Math

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N

knowledg e base

Associ ation Rules

Rule operation

Expert screening Temporar y library

Learning the database

Figure 8. Template-based complex event processing. Figure 8. Template-based complex event processing.

In complex event processing, the NFA is used to describe the push decision of an atomic event. In complex event processing, is used to the push decision of an atomic event. Depending on the business needs,the theNFA application of describe nondeterministic finite automata in complex Depending on the business needs, the application of nondeterministic finite automata in complex event processing is extended and innovated as follows: event processing is extended and innovated as follows: (1) When the life cycle of the automatic validation machine is overdue, the automatic machine (1) that When thenot lifecomplete cycle of the automatic machine is overdue, the automatic machine does dies, and the validation event request was cancelled. that does not complete dies, and the event request was cancelled. (2) During the concurrent event processing, the automaton calculates and matches the (2) matching During thesystem concurrent event processing, thethe automaton calculates andfor matches the matching separately, and provides suspending function event handling. system separately, and provides the suspending function for event handling. (3) For those automaton that “from suspend to die”, atomic events will be deposited into a (3) temporary For those automaton that “from suspend library for subsequent repair. to die”, atomic events will be deposited into a temporary library for subsequent repair. The complex event processing algorithm of the finite machine is described as follows: The complex event processing algorithm of the finite machine is described as follows: 1: e = GetEvent (); // Get the atomic event doGet the atomic event 1:2:eif= (Timeup GetEvent (e)) (); // 3: Cep Start With 2: if (Timeup (e)) do (e); // If the time of event e exceeds the verification period, a new periodic process is detected 3: Cep Start With (e); // If the time of event e exceeds the verification period, a new periodic process is detected 4: if Request wait (e) do 4:5:if Request wait//(e) do Send (e); If there is a request for an e event, an e event is sent 5:6: result Send (e); // If there is a for an e event, e event is sent = result Cep (e); request // for complex eventan processing 6:7:result = result Cep (e); // for complex event processing if Cep List push (e) do 7: if Cep List push (e) do 8: goto step 1; //determine the completion of the event is not processed and re-verified 8: goto step 1; //determine the completion of the event is not processed and re-verified 9: if result = Cep End do 9: if result = Cep End do 10: SendtotempDB (e); //complex event aborted, the event e stored in the memory database 10: SendtotempDB (e); //complex event aborted, the event e stored in the memory database 11: if result = CepSuccess 11: if result = CepSuccess 12: Outputresult Outputresult (resultCep (e)); //complex event processing terminate and 12: (resultCep (e)); //complex event processing operationsoperations to terminatetoand output critical output critical events events 13:goto gotostep step 13: 1; 1;

(3) Key Processes for Manufacturing Processes Based on Matching Templates (1) Heuristic Real-Time CEP of the Directed Graph To realize real-time active sensing of complex events by the CEP engine, we have developed a set of algorithms based on association templates to improve the speed of event detection and


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(3)

Key Processes for Manufacturing Processes Based on Matching Templates Due to the relatively fixed mechanism of automata and Petri nets in the CEP intermediate structure, is difficult to adjustCEP theof priority of tasks. We, thus, proposed a complex event structure (1) it Heuristic Real-Time the Directed Graph model according to event expression and the type of the manufacturing process. A directed graphic [49] To realize real-time active sensing complex events byprocessing. the CEP engine, we have complex developed a set is chosen to show the mapping modelofof complex event Specifically, event of algorithms based on association templates to improve the speed of event detection and correlation patterns in the library are mapped to a directed graph and stored in the memory database, where matching. One of graph the major contributions of this paper to develop methods to achievetoreal-time there are directed nodes, intermediate nodes, andisroot nodes, which correspond atomic processing of multi-level event instances by performing complicated event pattern matching according events, intermediate modes, and complex event modes. Identical nodes are merged, as shown in to the set task priorities, thus reducing the processing andPE facilitating active of Figure 9. There exist necessary relationships betweentime event (process real-time event) and CE sensing (complex key events. event). Duemid-point to the relatively fixed mechanism automata and Petri nets in the CEP intermediate The node performs complexofevent correlation matching according to the CEP structure, patterns it is difficult to adjust the priority of tasks. We, thus, proposed a complex event and transmits the result to the parent node when the atomic event is generated by thestructure complex model event according procedure to event expression and the typeWhen of thethe manufacturing process. directed graphic [49] processing of the directed graph. corresponding parent A node detects the input is chosen to show the mapping model of complex event processing. Specifically, complex event of the sub-node event, the CEP matching pattern in the corresponding node is searched and the patterns in the library mapped toIfathe directed graph and storedthe in the memory database, where there association process is are performed. match is successful, intermediate node outputs the are directed graph nodes, intermediate nodes, and root nodes, which correspond to atomic events, processed complex event to the parent node for subsequent association, and the root node outputs intermediate modes,event. and complex event Identicalevent nodes are merged, as shown in Figure the final event-aware Otherwise, themodes. corresponding is stored or discarded depending on9. There exist necessary the semantic judgment.relationships between event PE (process event) and CE (complex event). CE

Seq(Seq(And(e1,e2),e3),And(e2,e4)) Seq

Seq(And(e1,e2),e3)

And(e1,e2)

PE1

Seq

And

And(e2,e4)

And

PE2

PE3

PE4

Figure 9. Directed graph based on a complex event pattern. Figure 9. Directed graph based on a complex event pattern.

(2) Event Matching Based on the Heuristic CEP Algorithm The mid-point node performs complex event correlation matching according to the CEP patterns The essencethe of result our heuristic is to find the when shortest function defines theby path the and transmits to the parent node thepath atomic eventthat is generated thefrom complex sub-node to the parent node in a complex event pattern tree, which is used to search and evaluate event processing procedure of the directed graph. When the corresponding parent node detectsthe the optimal complex event processing scheme. The heuristic function considers two factors: I) when some input of the sub-node event, the CEP matching pattern in the corresponding node is searched and or allassociation of the sub-events node occur, the match patternismatching is carried out; II) thenode shorter the path the processofisthe performed. If the successful, the intermediate outputs the between processing the fewer thesubsequent event composition layers, androot thenode easier it is for processedevent complex event tonodes, the parent node for association, and the outputs the complex event pattern matching. Heuristic functions are defined as follows: final event-aware event. Otherwise, the corresponding event is stored or discarded depending on the semantic judgment.   H(n)  min rL(n)   W(n i )  (1) r  n iAlgorithm  (2) Event Matching Based on the Heuristic CEP W(n i )  k max  [P(n)/Q(n)  1](k max  k min ) (2) The essence of our heuristic is to find the shortest path function that defines the path from the sub-node to is the parent nodefunction in a complex event pattern tree, which used to search and evaluate where 𝐻(𝑛) the heuristic of node n. 𝑊(𝑛) denotes all setsisof paths from the node n to the optimal complex event processing The heuristic function considers (I) when each exit node, representing that nodescheme. 𝑛𝑖 is a node on the path r from node ntwo to afactors: particular exit some or all ofisthe sub-events of the the matching is carried out; (II) the shorterand the node. 𝑊(𝑛) a waiting function ofnode nodeoccur, n, used topattern ensure the efficiency of synchronous threads

programs. The value of W(n) is determined according to the value range of 𝑊(𝑛), [𝑘𝑚𝑎𝑥 , 𝑘𝑚𝑖𝑛 ], P(𝑛), and 𝑄(𝑛). P(𝑛) represents the number of sub-events that have occurred for the complex event pattern. 𝑄(𝑛) represents the minimum number of sub-events required to trigger the pattern matching. Our heuristic based complex event pattern matching algorithm is shown as follows:


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path between event processing nodes, the fewer the event composition layers, and the easier it is for complex event pattern matching. Heuristic functions are defined as follows: H(n) = minr∈L(n)

∑ W ( ni )

! (1)

ni ∈ r

W(ni ) = kmax − [P(n)/Q(n) − 1](kmax − kmin )

(2)

where H (n) is the heuristic function of node n. W (n) denotes all sets of paths from the node n to each exit node, representing that node ni is a node on the path r from node n to a particular exit node. W (n) is a waiting function of node n, used to ensure the efficiency of synchronous threads and programs. The value of W(n) is determined according to the value range of W (n), [k max , k min ], P(n), and Q(n). P(n) represents the number of sub-events that have occurred for the complex event pattern. Q(n) represents the minimum number of sub-events required to trigger the pattern matching. Our heuristic based complex event pattern matching algorithm is shown as follows: 1: Imports: subevent flow = [e1, e2, e3, ...] // input atomic event stream 2: Exports: complex event flow / / output according to complex event pattern matching the key event flow 3: Function Heuristic Search (Instance II ext) 4: For each subevent flow e { 5: hb set to null //initialization engine, the node queue hb is empty 6: n← e // Case e The corresponding node 7: For the parent node f of n{ 8: append e into the end of the cache queue at f 9: Put f into hb}} 10: While hb is not null { 11: n←The smallest node existed in hb 12: ce← the first event instance in the Buffer queue at node n / / event instance with priority to handle 13: ins ←new event instance or null generated by the ce pattern matching at node n 14: remove ce from the the cache queue n 15: Recalculate H(n) and all its children node H(ni ) 16: If ins null { 17: If n is an exit node { 18: append ins at the end of output queue of complex events 19: Else 20: For the parent node f of n{ 21: append ins at the end of the buffer queue of 22: save f into hb}}}} 23: If the instance cache queue of n is null { 24: remove n from hb}} 25: End function

(3) Real-time CEP Framework and its Working Mode Our real-time CEP engine was developed under the Eclipse environment using the Java language. The overall framework is shown in Figure 10. Atomic events are formed after the sensed data is cleaned and encapsulated. The atomic events are extracted by atomic event extractor and the atomic events stream is generated. At the same time, atomic events are standardized and encapsulated, and entered into the real-time CEP engine in XML format. The CEP engine receives the atomic event stream and simultaneously detects the complex event pattern and completes the event correlation matching. The complex event stream is exported at last. The domain expert can add or delete the matching template generated by association rules mining online.

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16 of 21 16 of 21 Complex event flow XML Socket output interface

associatio n rules

Real-time CEP engine

matching template

repository function calculator resolver

Complex Event Processing Based on Directed Graph

Complex Event Patterns

intermediate structure memory

The event instance queue for the node to be processed

Complex event caching Detection / execution module Event dispatcher

Realtime CEP services

Atomic event cache

XMLSocket input interface Atomic event flow

Figure 10. Framework of the real-time CEP engine.

The working of the The working principles principles of the real-time real-time CEP CEP engine engine associates associates atomic atomic events events with with complex complex event event patterns generated complex events. TheThe CEPCEP engine firstfirst buffers and patterns generatedby byassociation associationrules rulesand andoutputs outputs complex events. engine buffers distributes the atomic events. Then, the fast heuristic CEP algorithm based on the directed graph is and distributes the atomic events. Then, the fast heuristic CEP algorithm based on the directed implemented by the detection/execution module, and the complex events resulting from the graph is implemented by the detection/execution module, and the complex events resulting from the correlation matching the complex complex event complex events events correlation matching are are placed placed in in the event cache cache in in XML XML format. format. Finally, Finally, complex are exported to the information application system to provide decision support for the manufacturing are exported to the information application system to provide decision support for the manufacturing process. The the complex complex event event pattern pattern process. The real-time real-time CEP CEP service service evaluates evaluates the the latest latest response response time time of of the during the the detection detectionprocess processand andensures ensures the real-time performance of the event sensing. realduring the real-time performance of the event sensing. The The real-time time CEP engine communicates via two types of interfaces: I) XML documents are transmitted CEP engine communicates via two types of interfaces: (I) XML documents are transmitted through through XMLand Socket; andare II) transmitted data are transmitted through Javainvokes RMI that the event the XML the Socket; (II) data through Java RMI that theinvokes event processing processing methods in the engine remotely. methods in the engine remotely. In order to deal in the the CEP engine, domain experts generate In order to deal with with complex complex event event pattern pattern processing processing in CEP engine, domain experts generate event-matching templates and improving improving related related event-matching templates through through complex complex event event association association rule rule mining mining and algorithms, and transform them into XML-based complex event correlation matching description algorithms, and transform them into XML-based complex event correlation matching description templates, which which are are saved savedin inthe theknowledge knowledgebase. base.The Thecomplex complexevent eventcorrelation correlation matching template templates, matching template is is mapped by a parser into a complex event pattern, which is identified by a pattern-matching structure mapped by a parser into a complex event pattern, which is identified by a pattern-matching structure based on based on aa directed directed graph graph to tofacilitate facilitatethe thetransmission transmissionofofcomplex complexevent eventprocessing. processing. According to the framework framework of the real-time real-time CEP CEP engine, engine, the the real-time real-time CEP CEP engine engine operating operating According to the of the mode f is defined as follows: I). The atomic event stream is entered into the atomic event cache mode f is defined as follows: (I). The atomic event stream is entered into the atomic event cache of of the the CEP engine via the XML Socket interface. II) According to the event type and the control of the event CEP engine via the XML Socket interface. (II) According to the event type and the control of the event processing, the atomic event event dispatcher load the the atomic atomic event event into into the the corresponding corresponding node node processing, the atomic dispatcher is is used used to to load processing unit and cache the queue according to the heuristic function value. The heuristic function processing unit and cache the queue according to the heuristic function value. The heuristic function calculator isisused recalculate thethe heuristic function of theofnode node instance cache changes, calculator usedtoto recalculate heuristic function the when node the when the node instance cache and, thus, rearrange the node queue. III) The priority node is extracted in the queue through the changes, and, thus, rearrange the node queue. (III) The priority node is extracted in the queue through detection/execution module and the corresponding node in the node is deleted. Meanwhile, the node the detection/execution module and the corresponding node in the node is deleted. Meanwhile, processing unit script calculated. IV) The heuristic function calculator the the node processing unitisscript is calculated. (IV) The heuristic function calculatortogether togetherwith with the detection/execution module process the event instances until there are no event in the pending node detection/execution module process the event instances until there are no event in the pending node queue. A, event in the atomic event cache continues to load node queue queue. A,and andthe thepriority priority event in the atomic event cache continues to the load the processing node processing through the atomic event dispatcher. V) Complex events generated by the detection module module and the queue through the atomic event dispatcher. (V) Complex events generated by the detection CEP node are appended to the complex event cache and will be exported through the XML and the CEP node are appended to the complex event cache and will be exported through theSocket XML interface Socket interface 5. Deployment of Active Perception and Processing Technology for Manufacturing


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5. Deployment of xActive Perception Sustainability 2018, 10, FOR PEER REVIEW and Processing Technology for Manufacturing

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5.1. Application Development Platform 5.1. Application Development Platform To evaluate the effectiveness of the proposed ASPIE framework for active sensing and processing To evaluate the effectiveness of the proposed ASPIE framework for active sensing and of IoMT events, we developed a software platform for a local chili oil sauce manufacturing company processing of IoMT events, we developed a software platform for a local chili oil sauce manufacturing using the technology framework as shown in Figure 11. The object-oriented management platform was company using the technology framework as shown in Figure 11. The object-oriented management implemented using C# and Java with Microsoft SQL Server 2008 R2 database, Windows 7 operating platform was implemented using C# and Java with Microsoft SQL Server 2008 R2 database, Windows system, and the .NET 4.0 architecture. 7 operating system, and the .NET 4.0 architecture.

release Research on Data Sensing and Processing Technology of Manufacturing

Atomic event filtering

Data collector

Compoun d event matching

ERP

Event publishing

Event handling framework

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MES、ERP

Service interface layer Research on the Architecture of Manufacturing

Business services

Remote call interface

Original system

Business logic layer O / R mapping layer

Data processing module Data base sensor Development and Realization of Manufacturing

……

RFID equipment

Integrated use of database technology, network technology and related development language for system development

Figure 11. 11. Technology Technology framework framework for forASPIE ASPIEimplementation. implementation. Figure

5.2. System Implementation 5.2. System Implementation The ASPIE framework has to be embedded into the current ERP system at the LGM chili oil The ASPIE framework has to be embedded into the current ERP system at the LGM chili manufacturing company to evaluate how they can help to improve the management of the oil manufacturing company to evaluate how they can help to improve the management of the manufacturing processes. LGM has implemented the ERP system aiming to achieve manufacturing manufacturing processes. LGM has implemented the ERP system aiming to achieve manufacturing resource data perception by the heterogeneous sensing equipment. It can use data resource resource data perception by the heterogeneous sensing equipment. It can use data resource management and knowledge data mining and analysis of manufacturing data to provide data assets management and knowledge data mining and analysis of manufacturing data to provide data for business needs inside and outside the enterprise. Furthermore, data need to be shared in the cloud assets for business needs inside and outside the enterprise. Furthermore, data need to be shared to support ubiquitous collaborative manufacturing through the manufacturing resource in the cloud to support ubiquitous collaborative manufacturing through the manufacturing resource visualization and manufacturing resources service-on-demand. The whole dataflow and related visualization and manufacturing resources service-on-demand. The whole dataflow and related modules of LGM ERP is shown in Figure 12 with the ASPIE modules included. In this framework, modules of LGM ERP is shown in Figure 12 with the ASPIE modules included. In this framework, the production process sensing data and their processing is used to provide decision support for the production process sensing data and their processing is used to provide decision support for dynamic monitoring of discrete manufacturing processes for effective improvement of dynamic monitoring of discrete manufacturing processes for effective improvement of manufacturing manufacturing execution and management. execution and management.

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Real-time mapping

Cloud marketing Cloud marketing Cloud collaboratio n

Cloud Center

Cloud maintenance

network Centre ··· Supply Chain Partnership Application service system

LIS

ONS Server

ALE

Rules engine and rule base

Event Management

Equipment management

Device driver adaptation

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Enterprise database Local Informat ion Services

Enterprise application service system：MES、ERP、SCM等、

Enterprise database Inquire

Big data storage, analysis and processing

设备接口

Logistics tracking

RFID Reader

The bar code

RFID Label

Sensor

ONS server

Bar code scanning gun

Manufacturing management and control platform platform

Data Perception and Control Feedback

Remote monitoring Forecast and warning Remote diagnosis and maintenance

Warehouse Management

Production monitoring Environmental monitoring

PLC

Human computer interaction

Sencor

Intelligent Terminal

Traceability Quality Inspection

Figure 12. Production data flow and ASPIE module at LGM, a Chili oil manufacturer. Figure 12. Production data flow and ASPIE module at LGM, a Chili oil manufacturer.

According to the function modules of the manufacturing system platform and processing of data According to the function modules of the manufacturing and processing of data sensing, the applications of comprehensive data are analyzed system in Tableplatform 1. sensing, the applications of comprehensive data are analyzed in Table 1. Table 1. Event and data flow of LGM manufacturing processes. Table 1. Event and data flow of LGM manufacturing processes. Case Name Production Process IoMT Events Case Name Production Process IoMT Events Real-time video monitoring of production processes, real-time manufacturing Real-time video monitoring of production processes, real-time manufacturing environment, Display content environment, product quality inspection data, raw material quality data Display product quality inspection data, raw working materialconditions, quality data analysis, working analysis, equipment key businessequipment point checks. content conditions, key business point checks. (1) Real-time production video monitoring to obtain the (1) Real-time production video monitoring via video camera:via to video obtaincamera: the real-time status real-time status of production line. of production line. (2) Real-time monitoring of environmental data (via real-time sensors): (2) Real-time monitoring of environmental data (via real-time sensors): to obtain to obtain environmental monitoring information, including air environmental monitoring information, including air temperature, humidity temperature, humidity and combustible gas concentration.and combustible gas concentration. (3) Product quality data monitoring: to observe the trend graph of qualified product ratios. to observe the trend graph of qualified product ratios. (3) Product quality data monitoring: Basic event Raw material quality data analysis: to observe quality of raw (4) Raw material (4) quality data analysis: to observe the quality of rawthe materials and its flow materials and its changes at different time periods. changes at different time periods. Basic event flow (5) status Equipment work checking: to observe the workofstatus changes (5) Equipment work checking: tostatus observe the work status changes forklifts (we of forklifts (we used the forklift status information as example). used the forklift status information as example). (6) Key business point check: to check key information at the critical business (6) Key business point sites, check: to check key information at the critical business sites, including raw materials shelf life reminding, equipment status including raw materials shelf life to-do reminding, equipment status reminding, to-do task reminding, task reminding, information of the staff on site. reminding, information of the staff on site.

In In our our LGM LGM chili chili sauce sauce manufacturing manufacturing company company example, example, we we analyzed analyzed the the sensing sensing data data of of personnel, forklifts, an manufacturing environment to provide the production managers real-time personnel, forklifts, an manufacturing environment to provide the production managers real-time environment equipment information, and working conditions of forklifts, which provided environmentinformation, information, equipment information, and working conditions of forklifts, which


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decision support for effective control of the manufacturing environment of the workshop and resource scheduling. 6. Conclusions This paper proposed an active sensing and processing architecture (ASPIE) of IoMT complex events based on data-driven manufacturing processes, characteristics of IoMT, and complex event processing technology. ASPIE will be able to support production control and intelligent decision-making of manufacturing enterprises, which is composed of an active sensing method of the complex events in IoMT processes, a unified descriptive language of event models, and a template-oriented processing method for complex events. The proposed ASPIE framework has already been implemented and evaluated at a food manufacturer, which showed its feasibility and effectiveness. Considering the application requirements of intelligent manufacturing, the research in this paper can be extended in the following aspects: (1) To establish a highly-efficient interactive manufacturing data management system; (2) to study the parallel and efficient data mining algorithm for complex manufacturing events; (3) due to the complex relationship among the events of the IoMT processes, the event instance strategy and logic operation function needs to be further studied in the XEDL event model description language for a more unified and accurate description of the complex relationships between the objects, between the activities, or between the objects and activities contained in the manufacturing data; and (4) to construct big data application planning based on an intelligent manufacturing model Acknowledgments: This work is supported by the China Scholarship Council and the National Natural Science Foundation of China under grant nos. 91746116 and 51741101, and Science and Technology Foundation of Guizhou Province under grant no. Talents [2015]4011 and [2016]5013, Collaborative Innovation [2015]02. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research. We would also like to thank Qin Li and Hongjing Wei for their help in English writing of this paper. Author Contributions: S.L. and W.C. conceived of and designed the study. W.C, S.L., and Jianjun worked on the algorithm design. W.C., Jianjun, and S.L. wrote the manuscript. Jie made the figures and reformatted the manuscript. Jie and Jianjun revised and polished the manuscript. All authors have read and approved the final manuscript. Conflicts of Interest: The authors declare no conflict of interest.

References 1. 2. 3. 4. 5.

6. 7. 8.

Jardim-Goncalves, R.; Romero, D.; Grilo, A. Factories of the future: Challenges and leading innovations in intelligent manufacturing. Int. J. Comput. Integr. Manuf. 2017, 30, 4–14. Lu, Y.J.; Cecil, J. An Internet of Things (IoT)-based collaborative framework for advanced manufacturing. Int. J. Adv. Manuf. Technol. 2016, 84, 1141–1152. Yao, X.; Yu, M.; Chen, Y.; Xiang, Z. Connotation, architecture and key technologies of Internet of manufacturing things. Comput. Integr. Manuf. Syst. 2014, 20, 1–10. Hou, R.-C.; Ding, X.-Q.; Tao, Y.; Wang, L.-S.; Jing, R.-H.; Li, J.-H. Internet of manufacturing things and relevant technical architecture. Comput. Integr. Manuf. Syst. 2014, 20, 11–20. Potyrailo, R.A.; Surman, C.; Monk, D.; Morris, W.G.; Wortley, T.; Vincent, M.; Diana, R.; Pizzi, V.; Carter, J.; Gach, G.; et al. RFID sensors as the common sensing platform for single-use biopharmaceutical manufacturing. Meas. Sci. Technol. 2011, 22, 082001. [CrossRef] Brintrup, A.; Ranasinghe, D.; McFarlane, D. RFID opportunity analysis for leaner manufacturing. Int. J. Prod. Res. 2010, 48, 2745–2764. [CrossRef] Huang, G.Q.; Zhang, Y.F.; Jiang, P.Y. RFID-based wireless manufacturing for real-time management of job shop WIP inventories. Int. J. Adv. Manuf. Technol. 2008, 36, 752–764. [CrossRef] Poon, K.T.C.; Choy, K.L.; Lau, H.C.W. A Real-time Manufacturing Risk Management System: An Integrated RFID Approach. In Proceedings of the 2007 Portland International Conference on Management of Engineering & Technology, Portland, OR, USA, 5–9 August 2007.


9. 10. 11.

12.

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22.

23. 24.

25. 26. 27. 28. 29.

30. 31.

20 of 21

Barenji, R.V.; Barenji, A.V.; Hashemipour, M. A multi-agent RFID-enabled distributed control system for a flexible manufacturing shop. Int. J. Adv. Manuf. Technol. 2014, 71, 1773–1791. [CrossRef] Liukkonen, M. RFID technology in manufacturing and supply chain. Int. J. Comput. Integr. Manuf. 2015, 28, 861–880. [CrossRef] Silva-Faundez, J.A.; Duran-Faundez, C.; Melin, P.; Aguilera, C. An Anticipatory Control for a Flexible Manufacturing System Based on the Perception of Mobile Units Using WSNs. Int. J. Comput. Commun. Control 2015, 10, 702–717. [CrossRef] Zhang, Y.; Zhan, G.; Wang, J.; Sun, S.; Si, S.; Yang, T. Real-time information capturing and integration framework of the internet of manufacturing things. Int. J. Comput. Integr. Manuf. 2015, 28, 811–822. [CrossRef] Tao, F.; Zuo, Y.; Xu, L.D.; Zhang, L. IoT-Based Intelligent Perception and Access of Manufacturing Resource Toward Cloud Manufacturing. IEEE Trans. Ind. Inform. 2014, 10, 1547–1557. Jeschke, S.; Brecher, C.; Meisen, T.; Özdemir, D.; Eschert, T. Industrial Internet of Things and Cyber Manufacturing Systems; Industrial Internet of Things; Springer: Cham, Switzerland, 2017; pp. 3–19. Tao, F.; Cheng, J.; Qi, Q. IIHub: An industrial internet-of-things hub towards smart manufacturing based on cyber-physical system. IEEE Trans. Ind. Inform. 2017, 99. [CrossRef] Hu, J.W.; Lewis, F.L.; Gan, O.P.; Phua, G.H.; Aw, L.L. Discrete-Event Shop-Floor Monitoring System in RFID-Enabled Manufacturing. IEEE Trans. Ind. Electron. 2014, 61, 7083–7091. [CrossRef] Zhao, X.; Zhang, Y.; Sun, S.; Wang, J.; Si, S. Implementing method and key technology for IoT-based manufacturing execution system. Comput. Integr. Manuf. Syst. 2012, 18, 2634–2642. Nan, L.; Hu, K.; Zhu, Y. A Novel Distributed Complex Event Processing for RFID Application. In Proceedings of the Third International Conference on Convergence and Hybrid Information Technology, Busan, Korea, 11–13 November 2008; pp. 1113–1117. Li, Y. Correlation Solution of System Monitoring Events Analysis and Administration based on Complex Event Processing; Donghua University: Shanghai, China, 2012. Tanase, N.; Iosif, G.; Nedelcu, A.; Iordache, I.; Luchian, A.M.; Suciu, G.; Suciu, V.; Costea, E. Concurrent engineering application in microsystem technology. J. Sci. Arts 2017, 3, 597–610. Vaidehi, V.; Vasuhi, S.; Ganesh, K. Person tracking using Kalman Filter in Wireless Sensor Network. In Proceeding of the 2010 International Conference on Advanced Computering, Chennai, India, 14–16 December 2010; pp. 60–65. Gerald, G.K.; Boris, K.; Kurt, R. Cordies: Expressive event correlation in distributed systems. In Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems, Cambridge, UK, 12–15 July 2010; pp. 26–37. Romdhane, R.; Boulay, B.; Bremond, F.; Thonnat, M. Probabilistic recognition of complex event. In Proceeding of the ICVS 2011, LNCS 6962, Sophia Antipolis, France, 20–22 September 2011; pp. 122–131. Wang, Y.H.; Yang, S.H. Plan-based Distributed Complex Event Processing for RFID Application. In Proceedings of the International Conference on Computational Intelligence and Software Engineering, Wuhan, China, 11–13 December 2009; pp. 1–4. Wang, Y.H.; Cao, K.; Zhang, X.M. Complex event processing over distributed probabilistic event streams. Comput. Math. Appl. 2013, 66, 1808–1821. [CrossRef] Yao, Y.; Meng, C.; Wang, C.; Tian, Z.; Feng, D. Complex event processing in RFID-aided real-time production monitoring. Mod. Manuf. Eng. 2015, 1, 24–29. Allen, L.V.; Tilbury, D.M. Anomaly Detection Using Model Generation for Event-Based Systems without a Preexisting Formal Model. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 2012, 42, 654–668. [CrossRef] Dias, J.R.S.; Maia, C.A.; Lucena, V.F. Synchronising operations on productive systems modelled by timed event graphs. Int. J. Prod. Res. 2016, 54, 4403–4417. [CrossRef] Zaharia, M.; Xin, R.S.; Wendell, P.; Das, T.; Armbrust, M.; Dave, A.; Meng, X.; Rosen, J.; Venkataraman, S.; Franklin, M.J.; et al. Apache spark: A unified engine for big data processing. Commun. ACM 2016, 59, 56–65. [CrossRef] Congosto, M.; Basanta-Val, P.; Sanchez-Fernandez, L. T-Hoarder: A framework to process Twitter data streams. J. Netw. Comput. Appl. 2017, 83, 28–39. [CrossRef] Basanta-Val, P.; Fernández-García, N.; Wellings, A.J.; Audsley, N.C. Improving the predictability of distributed stream processors. Future Gener. Comput. Syst. 2015, 52, 22–36. [CrossRef]


32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49.

21 of 21

Stoica, I. Trends and challenges in big data processing. Proc. VLDB Endow. 2016, 9, 1619. [CrossRef] Basanta-Val, P.; Audsley, N.C.; Wellings, A.J.; Gray, I.; Fernández-García, N. Architecting time-critical big-data systems. IEEE Trans. Big Data 2016, 2, 310–324. [CrossRef] Lv, Z.; Song, H.; Basanta-Val, P.; Steed, A.; Jo, M. Next-generation big data analytics: State of the art, challenges, and future research topics. IEEE Trans. Ind. Inform. 2017, 13, 1891–1899. [CrossRef] Fang, J.; Huang, G.Q.; Li, Z. Event-driven multi-agent ubiquitous manufacturing execution platform for shop floor work-in-progress management. Int. J. Prod. Res. 2013, 51, 1168–1185. [CrossRef] Cugola, G.; Margara, A. Processing Flows of Information: From Data Stream to Complex Event Processing. ACM Comput. Surv. 2012, 44. [CrossRef] Zaytoon, J.; Lafortune, S. Overview of fault diagnosis methods for Discrete Event Systems. Annu. Rev. Control 2013, 37, 308–320. [CrossRef] Li, Y.; Chang, Q.; Biller, S.; Xiao, G.X. Event-based modelling of distributed sensor networks in battery manufacturing. Int. J. Prod. Res. 2014, 52, 4239–4252. [CrossRef] Dagkakis, G.; Papagiannopoulos, I.; Heavey, C. ManPy: An open-source software tool for building discrete event simulation models of manufacturing systems. Softw. Pract. Exp. 2016, 46, 955–981. [CrossRef] Nie, J.; Sun, R.; Deng, X. Uncertain Complex Event Processing in Precision Agriculture Based on Data Provenance Management. Trans. Chin. Soc. Agric. Mach. 2016, 47, 245–253. Li, X.; Gao, H.; Qiao, Y. Real-time Complex Event Processing Engine in Internet of Things. J. Chin. Comput. Syst. 2015, 36, 2047–2052. Chen, W.; Li, S. Implementation technology and method for active perception of critical events in IOMT-based manufacturing process. Manuf. Autom. 2015, 30, 148–152. Chen, W.; Li, S.; Huang, H. Active perception and management model for manufacturing data in discrete IoMT-based process. Comput. Integr. Manuf. Syst. 2016, 22, 166–176. Zhang, Y.; Sun, S.; Wang, J.; Si, S.; Yang, T. Embedded Device and Implementing Method for Multi-Source Information Perception. CN Patent 201310100948.X, 27 March 2012. Bai, A.; Tang, R.Z.; Lu, J.X. Semantic analysis of discrete shop logistics real-time data oriented to ubiquitous computing-based intelligent manufacturing. Comput. Integr. Manuf. Syst. 2012, 18, 550–559. Zhan, C.; Fan, Y. Complex event processing of real time enterprises based on smart items. Chin. J. Mech. Eng. 2007, 43, 22–32. Zhang, C.; Fan, Y. Complex event processing of manufacturing enterprises based on smart items. Comput. Integr. Manuf. Syst. 2007, 13, 2243–2253. Sun, L.; Li, Y.; Li, J. The CEP-based correlation solution of monitoring events on distributed IT resources. Comput. Appl. Softw. 2013, 30, 302–306. Kia, T.; Adrian, P. Semantic Rule-Based Complex Event Processing. Lect. Notes Comput. Sci. 2009, 5858, 82–92. © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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