Service Oriented Computing to Self-Learning

Service Oriented Computing to Self-Learning Production System M.K. Uddin, A. Dvoryanchikova, J.L. Martinez Lastra Tampere University of Technology, Finland [mohammad.uddin, aleksandra.dvoryanchikova, jose.lastra]@tut.fi

S. Scholze, D. Stokic

G. Cândido, J. Barata

Institut für angewandte Systemtechnik Bremen, Germany [scholze, dragan]@atb-bremen.de

UNINOVA - Instituto de Desenvolvimento de Novas Tecnologias, Potugal [gmc, jab]@uninova.pt

Abstract- The aim of this manuscript is to present what is SelfLearning production system and how service oriented architecture (SOA) and supporting technologies are bridged together to implement this new concept in the ongoing EU SelfLearning production system project. A brief review of the most recent EU projects that have reported results relevant to the main discussed investigation problems is presented. Reference architecture and functionalities of Self-Learning production system is introduced aiming for improved control and maintenance in production plants. Service oriented computing to Self-Learning production system is proposed to meet the required level of flexibility, interoperability and communications needs for reusable Self-Learning services. A roadmap for future research is defined. I.

INTRODUCTION

Technologies leveraging artificial intelligence at the factory floor, knowledge based system development and machine learning are being studied to make capabilities of self-X properties like self-adaptation, self-optimization, selfevolution and self-maintenance in production systems. This manuscript addresses Self-Learning production system, which is a new concept to apply cybernetic principles to derive intelligent production systems. The system selfadapt and learn in response to the dynamic changes in contextual information extracted from all factory levels. Context awareness approach addresses the integration of control and maintenance processes for necessary adaptation, which results in maintenance cost reduction and improves the overall equipment effectiveness especially regarding system availability and productivity. A reliable and secure software service based integration infrastructure using distributed networked embedded services in device space is the key to achieve such system. The main purpose of this work is to present what is SelfLearning production system and how SOA as an architectural paradigm and supporting technologies are bridged together to achieve a seamless enterprise wide connectivity using flexible, loosely coupled and reusable Self-Learning services. The contribution of this manuscript is three fold. Firstly, a brief review of the related research and exploitable results reported in the most recent EU projects are presented (Section II); and the bridging of SOA to the manufacturing world is addressed (Section III).

Secondly, the concept of Self-Learning production system is presented introducing the generic reference architecture. The functionalities of the architectural components are also described (Section IV). Finally, this work addresses service oriented computing to Self-Learning production system. The focus is to define a SOA-based communication infrastructure to provide universally accepted set of interoperability standards for building, describing, cataloguing and managing reusable SelfLearning services (Section V). The future research roadmap is outlined in section VI and the conclusions are drawn in section VII. II.

RELATED WORKS

To enable self-‘X’ features in production systems, researchers have considered modeling of cognitive behavior, control behavior and reactive behavior. Control strategy modeling using CAE tools, reactive/proactive behavior modeling with real time variants of state machines and cognitive behavioral modeling with UML based approaches is addressed in [1] to realize self-optimizing mechatronic systems. CAE tool CAMeL [2] and CASE tool Fujaba [3] is integrated at tool level and UML extension at the semantic level is addressed to achieve semantically rich interfaces at the component level to deal with multi agent systems. CRC 614 (Collaborative Research Center) [4] research addresses self-optimizing design and implementation issues on future electro-mechanical products having inherently partial intelligence. The primary objectives are the scientific exploration of self-optimizing principles on engineering products, design methods and required tools development. The aim is the realization of the principles on the level of hardware, system software and control software, which goes beyond the traditional rule based and adaptive control strategies. The results are further extended to design of systems, which are flexible, react autonomously and take actions in changing operating conditions. Principles of autonomous and adaptive control in assembly systems are addressed in [5] focusing on intelligent computational methods, agent based systems and reconfigurable manufacturing strategies. The goal is to utilize these principles to bring flexibility down to the factory floor. Architectural challenges of a self-managed system with a

three layer framework of component control, change management and goal management is addressed in [6]. The intent is to provide the required level of abstraction and deal with the challenges of scalable, flexible and robust systems. Context awareness and reasoning support through context modeling [7] where the knowledge is collectively generated, managed and updated is widely used in many application domains including manufacturing and production controls [8]. Application of effective learning support systems [9] and machine learning algorithms (e.g. reinforcement learning) [10] are still in the initial phase in wider scope of production environment. SOA-based Self-Learning systems have emerged in the recent years in several domains such as systems for classification of data from multiple sources (e.g. Zoomix solution) [11], data mining and recommendations (e.g.

Prudsys recommendation engine for ecommerce) [12], and automatic learning of repair strategies for web services [13]. The IBM Classification Module for OmniFind Discovery Edition [14] is also enabled in SOA environment. Several EU projects have reported relevant results which can be exploitable in SOA-based Self-Learning production system (table I). To summarize, Self-Learning production system addresses a novel approach to apply context awareness to allow for dynamic self-adaptation of production systems. Learning is perceived from analysing system behaviour and/or user’s action. This also results in radical increase of quality of services (QoS) embedded in the device space needed for such production systems within a scalable SOA-based infrastructure, which has not been explored up to now.

TABLE I SELF-LEARNING PRODUCTION SYSTEM AND SOA RELEVANT EU PROJECTS REVIEW Project Acronym SOA4All (NESSI) 2008-2011 AmI-4-SME (Joint Call) 2005-2008

Project Name

Project short description/focus

Service Oriented Architectures for All

SOCRADES (IST) 2006-2009 SODA (ITEA) 2006-2008

Service-Oriented Cross-layer infRAstructure for Distributed smart Embedded devices Service Oriented Device & Delivery Architecture

InLife (NMP2-CT) 2005-2008

Integrated ambient intelligence and knowledge based services for optimal life cycle impact of complex manufacturing and assembly line Innovative Ambient Intelligence Based Services to Support LifeCycle Management of Assembly and Manufacturing Systems Middleware for networked devices

Technical expertise, integration capabilities, ESB solutions within SOA, Dynamic WS composition, service mediation Technological and organizational approaches to enable manufacturing SMEs to use ambient intelligence technology for systematic innovation SOA implementation in next generation manufacturing systems in terms of device and applications integration SOA implementations targeted at multiple application domains such as industrial automation, home automation, telecommunication and automotive electronics Combination of ambient intelligence and knowledge management technologies to support maintenance and production control, reconfiguration of service based DPWS protocol based on SOA solutions Combination of AmI and semantic technologies promoting collaboration to support industrial installations and manufactured products Middleware for network embedded systems, AmI applications based on wireless devices, sensors through SOA and semantically enabled model driven architecture Development of service infrastructure for real time embedded networked applications

InAmI (Joint Call) 2005-2008 Hydra (IST) 2005-2009 SIRENA (ITEA) 2002-2005 SHAPE (ICT) 2007-2010 Q-ImPrESS (ICT) 2008-2010 NETQOS (IST) 2006-2008 AMIMOSES (ICT) 2008-2011

Ambient Intelligence Technology for Systemic Innovation in Manufacturing SME's

Service infrastructure for real time embedded networked applications Semantically-enabled Heterogeneous Service Architecture and Platforms Engineering Quality impact prediction for evolving service-oriented software Policy based management of heterogeneous networks for guaranteed QoS Ambient-intelligence interactive monitoring system for energy use optimization in manufacturing SMEs

Development and realization of enterprise system based on semantically enabled heterogeneous service architecture Bringing service orientation to critical application domain like industrial production control, telecommunication and critical enterprise applications Development of autonomous policy-based QoS management for wired/wireless heterogeneous communications networks Intelligent monitoring system for energy consumption for SMEs, knowledge based support system in SOA based platform

Relevant results for Self-Learning production system Exposing and consuming service via web principles, SOA integration, context management, semantic technologies Re-configuration of wireless networked devices based on contextual information Device level integration infrastructure within higher levels in SOA framework High level communications between devices based on SOA paradigm Secure transmission of AmI generated information

Strong focus on secure network communication in manufacturing industries Device level integration with SOA based infrastructure Bringing SOA to the device level. Integration of various models of processes, requirements, services in a platform independent manner SOA in industrial production control environment Automatic network level policy management SOA based platform for building different SW functionalities

III.

SOA IN MANUFACTURING

The possibility to incorporate intelligence even in the smaller devices via high performance microprocessors [15] has made possible knowledge-based, semantic web serviceenabled manufacturing. SOA deployed by Web Services (WS) has been recognized to answer the needs of a highly reconfigurable system: loose-coupling and dynamic discovery of new processes [16]. Traditional enterprise application technologies as Distributed Computing Environment (DCE), Common Object Request Broker Architecture (CORBA), Microsoft's Distributed Component Object Model (DCOM), Java 2 Enterprise Edition (J2EE) [17-21] are lacking from explicit platform-independency due to their own sets of communication standards and protocols. The integration of critical applications is within reach due to the adoption of WS and SOA. WS are currently supported by all major independent software vendors, including platform vendors such as IBM, Microsoft, SAP, PeopleSoft, Oracle, Sun, and BEA. Tool support for WSs and related technologies is growing. Although in the software world SOA is already widely adopted, SOA-compliant manufacturing is still an emerging paradigm. The drawbacks for several device-level SOA integration technologies such as for Jini [22] and UPnP [23] are: lack of platform neutrality, ill-adaptation to resource restricted devices and specific protocols for device discovery/eventing. Device Profile for Web Services (DPWS) is an extension of the Web Service protocol suite that defines the minimal set of implementation constraints to enable secure WS description, messaging, and dynamic discovery, publish/subscribe eventing at device level (Fig. 1). DPWS is equipped with WS standards (e.g. WSDL, XML schema, SOAP, WS-Addressing, WS-Metadata Exchange, WSTransfer, WS-Policy, WS-Security, WS-Discovery and WSEventing) that facilitate ideal integration at application, process and device level, application interoperability and reuse of IT assets. At the moment, DPWS is considered to be the most promising technology for implementation of SOA-compliant production systems. Pilots of DPWS-enabled devices in the industrial domain [24], [25], [26] are considered to be the first step towards achieving both horizontal collaboration and vertical integration. The possibility to expose real world devices with embedded software to standard higher level information systems is exploitable from SOCRADES integration architecture [27]. As illustrated in figure 2, enterprise applications interact with and consume data from wide range of networked devices within the application interface layer featuring core and extended web service standards. Orchestration of business processes is independent of the execution environment. A messaging system is able to consume any events and allows applications to interact with

Fig. 1. DPWS protocol stack.

devices that are intermittently connected. A service catalogue can be queried by users / applications so that service contracts are linked to running service instances. The management layer is in charge with monitoring and dynamic discovery of all devices, and is blind to the internal details of how (composite) services are executed in the networked devices. A. SOA-related technologies in production systems The most basic WS protocol is the industry standard XML, which is not only used as message data format, but also as foundation of all other WS protocols. WS rely on XML based WSDL, SOAP and UDDI for service description, transport and discovery. WSDL defines the service contracts to connect client to services. SOAP is the main standard to support transport. Service brokering is implemented via a UDDI registry. Service providers categorize entities using UDDI specifications for a core set of taxonomies (e.g. product code, industry code; service categorization). BPEL (Business Process Execution Language) and WSCI (Web Service Choreography Interface) allow composition of services to form (composed) business processes.

Fig. 2. SOCRADES (Service Oriented Cross-Layer Infrastructure for Distributed Smart Embedded Devices) - integration architecture [27].

IV.

SELF-LEARNING PRODUCTION SYSTEM

Self-Learning system is context aware and adaptive to changes in control and maintenance parameters in a dynamic manner. The system learns based on historical behaviour of run time adaptation and also from manual system configuration. Industrial requirements driven generic SelfLearning solution architecture hence addresses an integration of control and maintenance parameters in production plants, which in turns results in reduction of equipments down time, improved control through autonomous adaptation of control parameters (both feed-forward/feedback control loops) and increases the overall transparency of complex machines. Generic Self-Learning production system reference architecture is illustrated in figure 3. Self-Learning system has three must-have components: Context Extractor, Adapter and Learning Module. The Context Extractor is in charge with detection and interpretation of data from existing database systems, data servers, and file systems. These include plant specific process, equipment, enterprise information mostly in terms of XML files, NC programs (text files), and digital/analogue signals from sensors. The Adapter is in charge with real time adjustments of control and maintenance parameters, generation of maintenance plans, execution and identification of new parameters to be considered in the control loop. The Learning Module to learn relying on data mining and operator’s feedback to update execution of adaptation and context extraction at run time. The results are suggested to the Expert Collaboration UI, and must be backed up by the user. Operator feedback is registered to the learning module. An Expert Collaboration UI allows the user to be in the loop. Adapted solutions are suggested to be validated by user in the form of event messages, where the user can manually/automatically accepts/rejects any new solution. The user validation UI sends the feedback accordingly to the adapter and learning module. A Knowledge Configurator UI allows manual configuration of user inputs to the learning modules in order to enhance reliability and system performance. Evaluator allows the mapping of performance of adaptation and context extraction either manually with operator’s feedback or automatically against objective functions at run time. Evaluation results are sent to the learning module. Context identification rules and adaptation procedures are updated accordingly. A Data Access Layer makes wide range of data available in from the plant floor infrastructure. The Model Repository contains ontology based plant specific models for equipment, production processes and products. The models are shared by different software components at run time. The Context Repository allows update and storage of extracted/processed contextual information for later retrieval. Information flow among the modules is event driven in some cases and time based in other cases. Service Infrastructure is the foundation framework that ensures information is securely gathered from trusted context

Fig. 3. Self-Learning production system - Reference architecture.

data sources and that the control optimizations are securely communicated to control systems with appropriate levels of authentication. The communication authentication components ensure seamless and secure connectivity with existing manufacturing and information system communication protocols and security mechanisms, while overall monitoring is provided to ensure QoS requirements are achieved. Middleware allows flow of information from ERP level, devices or plant data servers to data access layer directly or via gateways/mediators depending on plant specific equipment and communication protocols. Context awareness is deployable in Self-Learning production system through intelligent agents. Agents utilize Ambient Intelligence (AmI) based sensory systems and embedded devices to monitor process and equipment parameters and extract context. Challenges associated to the implementation are the gathering of contextual information from sensors, the processing of context and the real-time middleware communication issues. The system provides solutions on how to extract context from networks/services and processes, how to apply selfadapting approaches and how to reuse these for highly reliable Self-Learning services in order to meet the quality attributes of responsiveness, adaptability and integration of control and maintenance parameters. V.

SOA AND SELF-LEARNING PRODUCTION SYSTEM

A powerful, reliable and secure communication infrastructure is needed to leverage the processing and realtime communication needs of the context extractor, adapter and learning module described in Section IV. The infrastructure must support efficient integration of control and maintenance activities and ensure adaptation/synchronization in real time. Extraction of critical contextual information from networks/services and processes, applying for selfadapting approaches and reusing these for highly reliable Self-Learning services are the required level of main information flow. These new services are integrated into the device centric infrastructure (Fig. 4.).

Self-Learning architecture is based on the service-oriented development approach that represent several different processes as services that are fully interoperable and allow further re-use for specific process reoccurrences. All layers of the Self-Learning concept are seen as a part of an overall service infrastructure so that every component is able to interact with each other but is additionally focused around specific transition services. In Self-Learning production system, service oriented integration is adapted at the device level to support applications from upper level. This vertical collaboration between the device level SOA and enterprise layer are perceived using middleware technologies in WS platform (Fig. 5). Services can be invoked directly to the web service enabled components, or via mediator/gateway to the non WS enabled components. Service oriented integration with the adoption of WS provides the core functional requirements for direct access to DPWS-enabled devices, direct access to back end services, automatic discovery of services and access those services, brokered access to events, service life cycle management issues, legacy devices integration and middleware device management [28]. This facilitates the holistic dynamic integration with the required level of interoperability, integration, reliability, security and communication in Self-Learning production system. A WS-based communication bridged with SOA is hence seen as the top candidate to cope with both enterprise-level

Fig. 5. SOA vision in WS platform for Self-Learning production system.

reaching beyond signaling context change, to the level of automatically understanding its meaning. Ontology-based reasoning about context will help devices to learn from each other. Context extraction is possible through RT information obtained from AmI sensors and information available in the existing plant floor databases. Applications of intelligent agents in Self-Learning middleware for context processing are perceived as entities which monitor events, process and notify to the subscribers (adapter module/learning module). Required agents’ integration with web services is possible via Web Service Integration Gateway (WSIG). This JADE [29] add-on provides support WSDL to ACL communications and vice versa, such as, agents acting as WS clients or WSs published as agent services and this can be achieved by using the directory facilitator (DF). Within an intelligent knowledge based system, learning is the most critical capability. Based on a model of the environment and an identified context, the system is able to predict the most probable future states of the environment. Learning is realized by applying data mining on historical behavior of the system and the operator’s action as well. The possibility to encapsulate the chosen mechanisms of service oriented computing and offer them as WS to the outside world will meet the requirements of holistic enterprise wide integration of Self-Learning production system. VI.

Fig. 4. SOA infrastructure for Self-Learning production system – ICT implementation view.

external uncertainties (e.g. random change in production order, product mix, volume, suppliers) and internal factors (e.g. equipment failures, fault tolerance, changes in process and equipment parameters, wear of tools, constraints handling). Context awareness is achievable via semantically enriched descriptions of WS. Structured ontologies provide semantic descriptions of production orders and processes, material handling and manufacturing workstations. Models of the equipment, products, processes and the environment allow

FUTURE RESEARCH

The future research roadmap will answer the key technological challenges concerning Self-Learning production system. Extraction of raw static and dynamic contextual information and necessary interpretation would be the feed for required adaptation. Self-learning services are intended to act as add-on generic solution in wider scope of modern production plants. In future, specification and development of early prototypes (EP) will be carried out for Context Extractor and Adapter module. Three different industrial use case scenarios will be used as testing platform for SOA-based integration of Self-Leaning EPs. Agents in WS platform will be developed as the middleware component for SOA infrastructure to provide context awareness. Plant specific QoS, security and

trust framework within SOA-based integration will also be taken into consideration. VII. CONCLUSIONS Recent advancements in embedded systems, computing, networking, WSs and SOA have opened the door for seamless integration of numerous plant floor devices to higher enterprise level applications. Several mentioned European projects have reported results of practical relevance concerning these technologies. Proposed Self-Learning production system architecture addresses assurance of effective self-adaptation and learning in production systems in order to improve control and maintenance utilizing context awareness. Scalable SOA deployed by WSs is selected as the top candidate paradigm to achieve flawless integration, interoperation and flexibility required for such innovative Self-Learning production system. ACKNOWLEDGMENT This work is supported by the ‘Self-Learning’ (Reliable Self-Learning Production Systems Based on Context Aware Services) project of European Union’s 7th Framework Programme, under the grant agreement no. NMP-2008228857. (www.selflearning.eu) REFERENCES  [1]

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