Improving Maintenance Processes with Distributed Monitoring Systems

Improving Maintenance Processes with Distributed Monitoring Systems Hans Fleischmann, Johannes Kohl, Jörg Franke Friedrich-Alexander University Erlangen Nuremberg {fleischmann|kohl|franke}@faps.uni-erlangen.de

Andreas Reidt, Markus Duchon, Helmut Krcmar fortiss GmbH {reidt|duchon|krcmar}@fortiss.org

Abstract-Industrial production systems meet strict requirements regarding availability, process control and condition monitoring. As a key enabler of Industry 4.0, cyber-physical systems form the core of a modular, web-based framework, which delivers more efficient condition monitoring mechanisms for maintenance staff in production facilities. The present approach illustrates the potential of a decentralized and centralized, distributed system by using standardized communication protocols and semantic information models. The use of webbased platforms like node.js and protocols such as OPC UA offers the ability to automate transferring information about anomalies, root causes and nominal data directly between cloudbased services and condition monitoring systems on the shop floor. To conclude, a validation is accomplished within a case study on a flexible handling unit.

MOTIVATION The continuing industrial revolution, known as Industry 4.0, is completely altering manufacturing industries. As enablers, Cyber-Physical Systems (CPS) and the growing Internet of Things (IoT) have immensely affected industrial value creation and the organization of work [1]. CPS represent a wide range of applications that involve digital processing components for interacting with the physical environment [2]. Their self-X capabilities allow the realization of cyber-physical production systems (CPPS), characterized by flexibility, autonomy and ergonomic operating conditions [3]. Improvements to sophisticated monitoring and autonomous optimization processes are imperative in order to control value creation efficiently. In today’s production systems, monitoring information technologies, such as Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP) Systems, greatly facilitate troubleshooting. Nevertheless, integrated industrial systems tasks are gradually shifting to decentralized CPS [4]. The expanding IoT leads to the successive dissolution of the classical hierarchical system arrangement (automation pyramid) to an automation cloud (see Fig. 1) [5]. However, there remain substantial gaps in networking physical assets and information systems - predominantly as regards maintenance- and monitoring-related functions, processes and data. Maintenance in a smart factory is particularly characterized by condition monitoring systems (CMS) with critical components and sophisticated assistance systems for maintenance workers, the features of CPS are not the central focus in this context. There continues to lack an integrated view of production resources, CMS or employees on the shop floor [6]. The interaction of intelligent CPS, cloud-based industrial software systems and the maintenance 978-1-5090-2870-2/16/$31.00 ©2016 IEEE

Fig. 1. From conventional automation hierarchy to an IoT-based automation cloud in smart factories [5]

staff has yet to have been adequately researched. The combination of human workers and CPS is accompanied by Socio-Cyber-Physical Systems (Socio-CPS), which enable human-integrated condition monitoring processes in smart factories [7]. This paper enhances a modular web framework for SocioCPS-based CMS [8]. Next, the literature review concerning industrial CPS, the focus lies in the system design of centralized and decentralized condition monitoring functionalities. We highlight new methodologies for CPSbased condition monitoring, state detection, fault diagnosis and predictive maintenance, particularly based on the methods of artificial intelligence. Based on the software design, we create a service-oriented CMS by using browser technologies (HTML5, JavaScript) and IoT-protocols (OPC Unified Architecture), which lays the foundation for this work’s discussion. As a final point, evaluation is shown in the realization of a condition monitoring system for a handling unit in a test cell. RELATED WORK A. Industrial Monitoring Systems Condition monitoring is the process of determining the conditions of machinery while in operation. The key to a feasible condition monitoring program includes [9]: (1) defining machine parameters that reveal the present state of machines, components or production processes, (2) evaluating contemporary data with prearranged nominal data and (3) precise fault diagnostics (see Fig. 2). In this context, model-based methods using pattern recognition have become increasingly important [10]. For the operation of CPPS, maintenance and CMS are important fields of research. Semantic information models remain one key aspect, and

377

Fig. 2. Theory behind Condition Monitoring Systems [8,9]

offer the ability to generate monitoring software based on requirements changed ad hoc. B. Cyber-Physical Systems CPS have emerged in many applications, such as avionic control systems, automotive electronics, industrial automation, and robotics. They analyze their environment and, based on observations, learn predictive models with methods of artificial intelligence. Typical tasks are condition monitoring, predictive maintenance and diagnostics. Architectural aspects are key to establishing collaboration between entities such as automation components, production machinery and software systems in a cyber-physical maintenance / condition monitoring scenario. Concerning CPS, several standards have been brought forth in public discussion [1]. In associated scientific work, abstract approaches as well as outlines for implementation that show the use of CPS for industrial automation and smart factories have been presented for reference architectures [9]. Within this vein, Plug-and-Produce refers to CPS’ ability to react to the existence of modified components and to consequently reconfigure themselves, primarily for manufacturing customized products, monitoring purposes and supervising control systems [11]. In this context, industrial CPS have parallels with system architectures for structural health monitoring and wireless sensor networks (WSN) [12]. For communication in CPS, the theory of Manufacturing Service Bus (MSB) familiarizes the idea of Service-Oriented Architectures (SOA) in the field of industrial fabrication [13]. C. Human-centric Condition Monitoring This paper expounds upon a modular framework for SocioCPS-based CMS in smart factories, which expands a modelbased condition monitoring approach (see Fig. 3). Requirements engineering, framework conceptualization and software design have been outlined in previous work (see Fig. 4) [8]. The developed framework consists of the following components: In the first step, data integration at the component level (e. g. sensors, actors, production data…) integrates relevant data for state detection. Subsequently, the specific state comparison and diagnosis functionalities are placed in a decentralized Condition Monitoring Engine (CME), which contains the business logic for condition monitoring, with separate layers for data import (Import),

Fig. 3. Development from Model-based Condition Monitoring to SocioCPS-based Condition Monitoring [8,10]

data management (Data Manager), data analysis (Analysis) and data export (Provider) of the results. The CME occurs decentralized at the machine level to minimize the load on industrial, real-time critical networks. Analyses provide data processing functionalities. Specific tasks are executed in submodules called Condition Monitoring Adapter (CMA), such as state comparison, diagnosis or predictive analytics. Monitoring mechanisms can be applied with adequate CMA in a structured manner. The configuration of the CME is possible due to the semantic description of the individual components to be monitored. To account for the human integration, it is indispensable to visualize analyses’ in an adaptable and platform-independent Human-Machine Interface (HMI). The CMS should primarily have the ability to diagnose errors and send messages to a user if an anomaly has been detected and self-repair is not possible. Therefore, HMI configuration, visualization items and their associations with data sources are carried out with a web server and the bi-directional WebSockets (WS)-protocol for the analytical data. Additionally, the results from condition monitoring are exportable into the IoT. Following the literature review in the field of industrial CPS and previous work, this paper expounds upon the system design by means of centralized and decentralized condition monitoring functionalities, CME and the associated cloud service. INTERACTION BETWEEN CENTRALIZED AND DECENTRALIZED CONDITION MONITORING IN CPS As the condition monitoring functionalities have thus far been performed completely decentrally in the CME, data exchange with a centralized cloud service is still useful. For example, in times of low traffic, updates, training and nominal data can be transferred between cloud-based services and the decentralized CME. The aggregated data can be used to perform cost-intensive calculations and analyses in an

378

Fig. 4. A Framework for Socio-CPS-Based Condition Monitoring [8]

adapted, scalable cloud location. This kind of synchronization and distribution of responsibilities can leverage innovative services such as predictive maintenance without interfering with the decentralized core aspect of the presented condition monitoring solution. The use of cost-intensive predictive maintenance mechanisms, which are usually heavily based on data mining techniques, is not adequate for lightweight computing units located within production machines. Instead, cost-intensive calculations on aggregated data can be efficiently performed centrally in the cloud service. Only decisions based on these essential findings should be implemented on/with the lightweight components. The architectural idea behind this distribution is a separation between a so-called learner and a classifier. The classifier is a lightweight, decentral analytical method for detecting potential failures or malfunctions based on learned behavior before they happen. During the learning phase, data e.g. logs are scanned to detect relevant patterns. Depending on the complexity of the classifier, the method can be integrated into the actual condition monitoring or deployed as a standalone component to perform predictive maintenance based on a vast amount of data. The learner, however, is located in the cloud service, where calculations are less expensive and where data are aggregated from an undefined number of machines. Data mining algorithms can be applied to historical data to identify patterns that could lead to malfunctions. In order to predict faults a classifier is trained in pattern recognition based on data from the connected

sensors or historical log files. Subsequently these classifiers are deployed to connected CME. This process is repeated when new data is gathered at the central location. After a relevant amount of additional historical data is collected, the learner is scheduled to start a new iteration of classifier production. However, to avoid unnecessary update cycles the classifiers are only updated and deployed to the connected devices when a significant difference between the predecessor is identified. The entire process is summarized in Figure 5. The same mechanism can be applied for training existing condition monitoring mechanisms in order to leverage the higher amount of available data. As a consequence, a single CPS is trained continuously by a central intelligence. The decentralized components are able to share information and knowledge via a cloud service without being forced to exchange data constantly. Additionally, the condition monitoring functionalities can be performed offline, independent from cloud-based systems, which leads to a high level of resilience concerning connectivity problems. Furthermore, all connected devices can benefit from the data available from each connected machine. This procedure integrates humans, as they are capable of controlling the transmission of nominal data and condition monitoring models between the CME and the cloud service. Afterwards, the generation process of the nominal data and subsequently the transfer of the nominal data to other CME can be triggered. Together with the connection to the cloud service, the aforementioned method of synchronizing data and exchanging responsibilities can also be implemented in further use cases within the IoT. CASE STUDY With the objective of validating the interaction between centralized and decentralized system components, we established the CMS in a modularized test cell (see Fig 6.). As a flexible test machine, the system contains a six-axis robot unit, which transfers mechatronic products among inspection modules. These are designed for a certain range of automotive control units and can be applied at various stages of production (sampling, functional testing, etc.). The test system can be implemented as a stand-alone or an inline solution, both with manual and robotic operation capabilities that consider variable production scales. Based on an increasing demand for equipment with resource-efficiency functions, an intelligent, self-controlling system with the following components was developed. For state detection, electrical and pneumatic sensors had been integrated in the test cell. The dynamic handling unit proposes a high number of possible movement patterns and serves as a suitable object for the setup of a condition monitoring situation. For this purpose, a Socio-CPS-Based CMS within the framework outlined above was constructed. The CME executes all sub-steps of the condition monitoring. In this environment, the communication standard Open Platform Communications Architecture (OPC UA) represents

379

Fig. 6. Handling robot in the test system Fig. 5. Concept of a cloud based classifier generation process

an interoperable technology for the development of industrial IoT applications. It is designed for data exchange between field components and related software systems in interconnected production grids. In comparison to other IoTprotocols such as Message Queue Telemetry Transport (MQTT), OPC UA offers tools for implementing objectoriented information models that explicitly support CPSmechanisms, e.g. self-configuration. Complex CMS, CPS and multi-layer structures can be modeled, as data structures are defined in interoperable profiles. Additionally, OPC UA is employable in combination with a wide array of hardware and operating systems. For secure data exchange in IoT, mechanisms for publish-subscribe are available. The node.js framework for network applications lays the foundation for the implementation of the CME and the cloud service. Gonzalez [14] shows the capabilities and boundaries of node.js as a runtime environment for event-driven software systems. Parameters for diagnosing various anomalies, inefficiencies and faults of the test cell were derived from information pertaining to energy and pneumatic consumption rates [8]. For example, the movement of the six-axis robot is driven by servo motors. The control system is a computer that allows precise movement by controlling the power supplied to the motors. Ongoing shifts in electrical and pneumatic consumption are an indication of wear, discrepancies and anomalies, but cannot be detected with conventional CMS methodologies. For this purpose, the OPC UA-based semantic information model was enhanced with a precise production state detection and related type classes for condition monitoring models and target data profiles. A. Interaction between centralized and decentralized components For the data exchange between the CME and the cloud service, it is necessary to merge relevant data for state detection. Sensor data from the measurement units are exposed in sufficient type classes via node-opcua, which represents an OPC UA implementation for node.js. To transfer state data to the cloud service, standardized publishsubscribe mechanisms in the OPC UA Data Access

specification, so-called subscriptions, are installed for the relevant data items. For deployment, the use of Docker allows executing the CME and the cloud service on various runtimes and computing platforms. In real-world applications, the cloud service is decentralized from the machine and the CME itself in a scalable cloud platform. For the deployment of the CME, we used a Raspberry Pi single board computer, for the cloud service, the Heroku Cloud Application Platform. In accordance with Socio-CPS, the condition monitoring system transmitted the results to web browsers via the Provider module by using the JS-Library socket.io. VALIDATION With regard to the system’s requirements, generalized methods for predicting and detecting faults were investigated. In the present case study, we focused on the implementation of two CMA in order to validate the applicability of condition monitoring and predictive maintenance based on a decentralized CME and a centralized cloud service. To implement both CMA, we used the Fast Artificial Neural Network (FANN) library, which offers the creation, training and testing of multi-dimensional Artificial Neural Networks (ANN) with different training methods and parameters. The node.js-package node-fann, which represents language binding and an API for the FANN library, integrates FANN into an CMA. Furthermore, node-fann in particular optimizes ANN parameters. A. CMA for Condition Monitoring Firstly, we implemented a CMA for condition monitoring on the basis of model-based condition monitoring. The basic principle behind this method is that a CPS learns the connection between sub-processes, condition monitoring models and their nominal (sensor) data patterns. The approach differs among altered stages of escalation, e.g. warning and action limits. This methodology is affected by the characteristics of highly dynamic data as the constant limitations of conventional condition monitoring are replaced with dynamic control limits. According to the theory of model-based condition monitoring, specific models must be generated in the first step. Therefore, it is necessary to integrate the operating data

380

TABLE I.

Cloud Service

Connected Devices

node.js – instance on Heroku Platform

OPC UA Condition Monitoring Engine

PARAMETERS OF THE ARTIFICIAL NEURAL NETWORK

ANN structure of energy profiling for model-based condition monitoring Approach #1 #2

Training Data / Historical Data

Input layer neurons

Condition monitoring components and parameters are set in advance 2

Selection of components and parameters for condition monitoring by the operators 7

Outpout layer neurons

1

1

Hidden layers

1

1

Hidden layer neurons

128

128

Training algorithm

Rprop (backpropagation)

Activation function Training cycles

elliot_symetric (fast symmetric sigmoid activation function) 1000

Accepted error delta

0.008

Import Data Manager

Learner

R

HTTP

Data Interface

node-fann

Analysis R

HTTP

Nominal Data / Classifiers

nodeexpress Provider

Web Server

Web Server Dashboard

OPC UA IoT

WebSockets

HTTP

nodeexpress

R

HMI

Fig. 7. Detail of the Cloud Service for Condition Monitoring

of the handling unit as well as the sensor data in the OPC UA information model. For the creation of the condition monitoring models, the learning process gathers data in a training phase from several nominal runs. Upon transferring the training data to the cloud service, the learner trains a classifier in the form of an ANN, which considers the different transition states and condition monitoring models of the robot. Table 1 shows the parameters of the ANN training for energy profiling as applies to the structure of the ANN itself. While approach #1 utilizes two input neurons (subprocess, relative process time) in order to approximate specific energy profiles, approach #2 uses one input neuron for the activation states of the sub-processes and one input neuron for the relative process time. As an output neuron, the electrical power consumption of the unit is used in both cases. Fig. 9 presents the training of the described ANN structures, which entail the approximate electricity consumption rates of the robot’s transitions. It is apparent that the deviations and peaks can be depicted more sophisticatedly. After the transfer of the classifier back to the CME, the classifier can be executed in the CME for condition monitoring. A change in the actual activity of the handling unit triggers the design of a new condition monitoring model by running an ANN with a subsequent state comparison of the live data and the warning and error intervals. Detected anomalies can be corrected by the maintainers subsequently. Regarding the state comparison, a frequency of 10 Hz was able to be reached, which is sufficient for detecting anomalies in production systems with the measurement devices used. By continuously adding the information of the maintainer to the detected events of the CMS, systematic knowledge management is operated. The maintainer externalizes his knowledge by creating diagnoses and maintenance and action instructions, which are integrated in the Socio-CPS (see Fig. 8). B. CMA for the Classification of errors Secondly, we implemented a CMA in order to execute predictive maintenance and diagnose specific errors on the

basis of various state parameters. To address these challenges, we used also concepts from the area of machine learning. One of these methods is that of classification, which is the problem of classifying new pairs of state parameters into a given set of categories. This is established on the basis of a training set of pairs whose categorization is known by the use of a classifier. In our case, we assigned a given set of observed state parameters to various error classifications for a given component. The concept also relies on an ANN to produce a classification method. In terms of how the ANN is constructed, we used input neuron s for every relevant state parameter and one output neuron for error detection. By running the ANN in a CMA, the system was able to detect which combination of parameters and log data led to specific errors. In order to improve the accuracy of the classifier, the maintenance worker acknowledges or rejects each error or warning decision from the classifier. By applying this methodology, it is possible to create a training data set, which contains various patterns in log files and state parameters. SUMMARY Industry 4.0 is the starting point for sophisticated business models and new services along value chains. In this context, software systems, maintenance and condition monitoring are relevant use cases for CPS. By crosslinking operating data and tacit knowledge about maintenance processes, Socio-

381

Action Diagnosis

Act

Event

Ability

+ Instruction

Data Points

Knowledge

+ Experience

Information

+ Condition Monitoring

Fig. 8. Integration of the tacit knowlegde in Socio-CPS-based Condition Monitoring

Fig. 9. Generated nominal values for different transitions of the handling robot on the basis of Artificial Neural Networks.

CPS can provide valuable insights in machine operation and help to improve maintenance processes. The advanced SocioCPS CMS delivers significant inputs for the development of CMS. With the classifier and learner method, a practical approach to the interaction between centralized and decentralized systems could be achieved. On a larger scale, the results can be used as the basis for the automatic generation of maintenance intervals and corrections for production plans and for specific stakeholders. Through semantic communication technologies and a high degree of modularity, the system can be integrated easily in CPPS. Future work will concentrate on the engineering of sophisticated user interfaces and complex event processing mechanisms for the detection of complex anomalies.

[5]

REFERENCES

[11]

[1] [2]

[3]

[4]

P. J. Mosterman, J. Zander, “Industry 4.0 as a cyber-physical system study,” Software & Systems Modeling, 2016, pp. 1–13. E. A. Lee, “Cyber physical systems: Design Challenges,” Proceedings of the 11th IEEE International Symposium on Object and ComponentOriented Real-Time Distributed Computing (ISORC), Orlando, 2008, pp. 363-369. S. Iarovyi, L. Martinez, L. Jose, R. Haber, R. del Toro, “From artificial cognitive systems and open architectures to cognitive manufacturing systems,” Proceedings 13th IEEE International Conference on Industrial Informatics (INDIN), Cambridge, 2015, pp. 1225-1232. A. W. Colombo, T. Bangemann, S. Karnouskos, J. Delsing, P. Stluka, R. Harrison, Industrial cloud-based cyber-physical systems, The IMCAESOP Approach, 2014.

[6] [7] [8] [9] [10]

[12] [13]

[14]

382

Powered by TCPDF (www.tcpdf.org)

O. Givehchi, H. Trsek, J. Jasperneite, “Cloud computing for industrial automation systems—A comprehensive overview,” Proceedings 18th Emerging Technologies & Factory Automation (ETFA), 2013, pp. 1-4. M. Broy, M. V. Cengarle, E. Geisberger, “Cyber-physical systems: imminent challenges,” Large-Scale Complex IT Systems Development, Operation and Management, Springer, 2012, pp. 1–28. E. M. Frazzon, J. Hartmann, T. Makuschewitz, B. Scholz-Reiter, “Towards Socio-Cyber-Physical Systems in Production Networks,” Procedia CIRP 7, 2013, pp. 49-54. H. Fleischmann, J. Kohl, J. Franke, “A Modular Web Framework for Socio-CPS-Based Condition Monitoring,” Proceedings 12th IEEE World Conference for Factory Communication Systems, Aveiro, 2016. VDMA – German Engineering Association, “VDMA 24582: Fieldbus Neutral Reference Architecture for Condition Monitoring in Factory Automation,” 2014. O. Niggemann, V. Lohweg, “On the Diagnosis of Cyber-Physical Production Systems: State-of-the-Art and Research Agenda,” Proceedings 29th AAAI Conference on Artificial Intelligence, Cambridge, 2015, pp. 4119-4126. R. A. Dionisio, R. Peres, J. Barata, “An agent based monitoring architecture for plug and produce based manufacturing systems,” Proceedings 13th IEEE International Conference on Industrial Informatics (INDIN), Cambridge, 2015, pp. 1318-1323. P. Rawat, K. D. Singh, H. Chaouchi, J. M. Bonnin, “Wireless sensor networks: a survey on recent developments and potential synergies,” The Journal of supercomputing, 68 (1), 2014, pp. 1–48. T. D. Nielsen, S. A. Neumann, T. L. King, “A methodology for managing model extensions when using the common information model for systems integration,” Power & Energy Society General Meeting (PES), 2009, pp. 1–5. D. Gonzalez, Developing Microservices with Node.js. Birmingham, Packt Publishing, 2016.