Development of machine learning strategies for fault diagnosis in virtual plants (Digital Twin)

En aquest projecte, s’ha validat la possibilitat de realitzar la monitorització de dades i el diagnòstic d’errors en línia (mentre s’executa la simulació) d’una planta química simulada (Digital Twin, en Anglès). La simulació es troba funcionant a un ordinador remot, mentre que s’accedeixen als resultats de la monitorització de dades i el diagnòstic d’errors per mitjà de l’accés, amb un ordinador personal, al núvol, més conegut com a ‘Cloud’ pel seu terme en Anglès. En primer lloc, s’explica la implementació, mòdul a mòdul, del prototipus modular proposat i emprat per a l’intercanvi d’informació des del ‘Digital Twin’ cap al núvol, el qual permet la monitorització de dades. Per a cada mòdul, s’introdueixen els programes o eines de programació necessaris per a la creació i/o execució. Les raons considerades alhora d’escollir aquests programes o eines de programació també s’exposen. A més a més, s’introdueix la plataforma on s’allotja el núvol junt amb els diferents servicis que ofereix el núvol, els quals han sigut utilitzats per a mostrar els resultats de la monitorització de dades. En segon lloc, els algorismes d’aprenentatge automàtic (Machine Learning en Anglès) i d’anàlisi de dades (Data Analysis en Anglès), que han sigut implementats pel diagnòstic d’errors en la planta simulada, són comentats des dels punts de vista teòric i d’implementació. També, s’explica el desenvolupament de les eines de monitorització per a la diagnosi d’errors, les quals són el resultat de combinar els algorismes anteriors amb el prototipus modular encarregat de l’intercanvi d’informació. Finalment, es documenta una prova de concepte del prototipus global que permet demostrar que aquestes tecnologies son factibles i fiables per a la realització de la monitorització de dades i el diagnòstic d’errors. Addicionalment, s’inclouen pautes a seguir per a millorar el prototipus.En este proyecto, se ha validado la posibilidad de realizar la monitorización de datos y el diagnóstico de errores en línea (mientras se ejecuta la simulación) de una planta química simulada (Digital Twin en Inglés). La simulación se encuentra funcionando en un ordenador remoto, mientras que se accede a los resultados de la monitorización de datos y el diagnóstico de errores por medio del acceso, con un ordenador personal, a la nube, más conocida como ‘Cloud’ por su término en Inglés. En primer lugar, se explica la implementación, módulo a módulo, del prototipo modular propuesto y empleado para el intercambio de información desde el ‘Digital Twin’ hacia la nube (Cloud), lo que permite la monitorización de datos. Para cada módulo, se introducen los programas y herramientas de programación necesarios para crear y/o ejecutar el módulo. Las razones para seleccionar los programas y las herramientas también son expuestas. Además, se introduce la plataforma donde se aloja la nube empleada junto con los diferentes servicios disponibles en la nube, los cuales se han usado para mostrar los resultados de la monitorización de datos. En segundo lugar, los algoritmos de aprendizaje automático (Machine Learning en Inglés) y de análisis de datos (Data Analysis en Inglés), implementados para el diagnóstico de fallos, se comentan desde los puntos de vista teórico y de implementación. Además, se explica el desarrollo de las herramientas de monitorización para el diagnóstico de fallos, que consiste en la combinación de los anteriores algoritmos con el prototipo modular encargado del intercambio de información. Finalmente, se documenta una prueba de concepto del prototipo en global, que demuestra que estas tecnologías son factibles y fiables para la monitorización de datos y el diagnóstico de fallos. Adicionalmente, se incluyen unas pautas a seguir para mejorar el prototipo.In this project, the possibility of performing the on-line data monitoring and fault diagnosis over a simulated chemical plant (Digital Twin) has been validated, which is running on a remote computer, by accessing the Cloud with a personal computer. Firstly, the implementation is explained, module by module, of the modular prototype proposed and employed for the exchange of information from the Digital Twin to the Cloud, which enables the data monitoring. For each of the modules, the programs or programming tools required for its creation and/or execution are introduced. The reasons for its selection are also exposed when explaining each of the modules. Moreover, the Cloud Platform chosen is also introduced together with the different services associated with it that have been used for displaying the results from data monitoring. In the second place, the Machine Learning and Data Analysis algorithms implemented for the fault diagnosis are commented from the theoretical and the implementation points of view. Furthermore, the development of monitoring tools for fault diagnosis is also explained, which consists of the coupling between the algorithms and the modular prototype for the exchange of information. Finally, it is documented a proof of concept of the global prototype, which demonstrates the feasibility and reliability of these technologies for performing data monitoring and fault diagnosis. Additionally, guidelines for the further development of the prototype are provided

Implementation of Service Orchestrated control procedures in OPC UA for JGrafchart

The automation industry is facing many challenges with higher demands on their production process. Technology used today does not allow for fast changes in the production line. This thesis will investigate how services can be modelled using a new standard OPC UA for data exchange. Encapsulation of the mechatronic functions as services will allow for creating control software using a SOA approach. An experimental set-up will investigate how an OPC UA server and client are created

A cyber-physical machine tools platform using OPC UA and MTConnect

Cyber-Physical Machine Tools (CPMT) represent a new generation of machine tools that are smarter, well connected, widely accessible, more adaptive and more autonomous. Development of CPMT requires standardized information modelling method and communication protocols for machine tools. This paper proposes a CPMT Platform based on OPC UA and MTConnect that enables standardized, interoperable and efficient data communication among machine tools and various types of software applications. First, a development method for OPC UA-based CPMT is proposed based on a generic OPC UA information model for CNC machine tools. Second, to address the issue of interoperability between OPC UA and MTConnect, an MTConnect to OPC UA interface is developed to transform MTConnect information model and its data to their OPC UA counterparts. An OPC UA-based CPMT prototype is developed and further integrated with a previously developed MTConnect-based CPMT to establish a common CPMT Platform. Third, different applications are developed to demonstrate the advantages of the proposed CPMT Platform, including an OPC UA Client, an advanced AR-assisted wearable Human-Machine Interface and a conceptual framework for CPMT powered cloud manufacturing environment. Experimental results have proven that the proposed CPMT Platform can significantly improve the overall production efficiency and effectiveness in the shop floor

Machine Tool Communication (MTComm) Method and Its Applications in a Cyber-Physical Manufacturing Cloud

The integration of cyber-physical systems and cloud manufacturing has the potential to revolutionize existing manufacturing systems by enabling better accessibility, agility, and efficiency. To achieve this, it is necessary to establish a communication method of manufacturing services over the Internet to access and manage physical machines from cloud applications. Most of the existing industrial automation protocols utilize Ethernet based Local Area Network (LAN) and are not designed specifically for Internet enabled data transmission. Recently MTConnect has been gaining popularity as a standard for monitoring status of machine tools through RESTful web services and an XML based messaging structure, but it is only designed for data collection and interpretation and lacks remote operation capability. This dissertation presents the design, development, optimization, and applications of a service-oriented Internet-scale communication method named Machine Tool Communication (MTComm) for exchanging manufacturing services in a Cyber-Physical Manufacturing Cloud (CPMC) to enable manufacturing with heterogeneous physically connected machine tools from geographically distributed locations over the Internet. MTComm uses an agent-adapter based architecture and a semantic ontology to provide both remote monitoring and operation capabilities through RESTful services and XML messages. MTComm was successfully used to develop and implement multi-purpose applications in in a CPMC including remote and collaborative manufacturing, active testing-based and edge-based fault diagnosis and maintenance of machine tools, cross-domain interoperability between Internet-of-things (IoT) devices and supply chain robots etc. To improve MTComm’s overall performance, efficiency, and acceptability in cyber manufacturing, the concept of MTComm’s edge-based middleware was introduced and three optimization strategies for data catching, transmission, and operation execution were developed and adopted at the edge. Finally, a hardware prototype of the middleware was implemented on a System-On-Chip based FPGA device to reduce computational and transmission latency. At every stage of its development, MTComm’s performance and feasibility were evaluated with experiments in a CPMC testbed with three different types of manufacturing machine tools. Experimental results demonstrated MTComm’s excellent feasibility for scalable cyber-physical manufacturing and superior performance over other existing approaches

Machine Tool Communication (MTComm) Method and Its Applications in a Cyber-Physical Manufacturing Cloud

The integration of cyber-physical systems and cloud manufacturing has the potential to revolutionize existing manufacturing systems by enabling better accessibility, agility, and efficiency. To achieve this, it is necessary to establish a communication method of manufacturing services over the Internet to access and manage physical machines from cloud applications. Most of the existing industrial automation protocols utilize Ethernet based Local Area Network (LAN) and are not designed specifically for Internet enabled data transmission. Recently MTConnect has been gaining popularity as a standard for monitoring status of machine tools through RESTful web services and an XML based messaging structure, but it is only designed for data collection and interpretation and lacks remote operation capability. This dissertation presents the design, development, optimization, and applications of a service-oriented Internet-scale communication method named Machine Tool Communication (MTComm) for exchanging manufacturing services in a Cyber-Physical Manufacturing Cloud (CPMC) to enable manufacturing with heterogeneous physically connected machine tools from geographically distributed locations over the Internet. MTComm uses an agent-adapter based architecture and a semantic ontology to provide both remote monitoring and operation capabilities through RESTful services and XML messages. MTComm was successfully used to develop and implement multi-purpose applications in in a CPMC including remote and collaborative manufacturing, active testing-based and edge-based fault diagnosis and maintenance of machine tools, cross-domain interoperability between Internet-of-things (IoT) devices and supply chain robots etc. To improve MTComm’s overall performance, efficiency, and acceptability in cyber manufacturing, the concept of MTComm’s edge-based middleware was introduced and three optimization strategies for data catching, transmission, and operation execution were developed and adopted at the edge. Finally, a hardware prototype of the middleware was implemented on a System-On-Chip based FPGA device to reduce computational and transmission latency. At every stage of its development, MTComm’s performance and feasibility were evaluated with experiments in a CPMC testbed with three different types of manufacturing machine tools. Experimental results demonstrated MTComm’s excellent feasibility for scalable cyber-physical manufacturing and superior performance over other existing approaches

A Framework for Industry 4.0

The potential of the Industry 4.0 will allow the national industry to develop all kinds of procedures, especially in terms of competitive differentiation. The prospects and motivations behind Industry 4.0 are related to the management that is essentially geared towards industrial internet, to the integrated analysis and use of data, to the digitalization of products and services, to new disruptive business models and to the cooperation within the value chain. It is through the integration of Cyber-Physical Systems (CPS), into the maintenance process that it is possible to carry out a continuous monitoring of industrial machines, as well as to apply advanced techniques for predictive and proactive maintenance. The present work is based on the MANTIS project, aiming to construct a specific platform for the proactive maintenance of industrial machines, targeting particularly the case of GreenBender ADIRA Steel Sheet. In other words, the aim is to reduce maintenance costs, increase the efficiency of the process and consequently the profit. Essentially, the MANTIS project is a multinational research project, where the CISTER Research Unit plays a key role, particularly in providing the communications infrastructure for one MANTIS Pilot. The methodology is based on a follow-up study, which is jointly carried with the client, as well as within the scope of the implementation of the ADIRA Pilot. The macro phases that are followed in the present work are: 1) detailed analysis of the business needs; 2) preparation of the architecture specification; 3) implementation/development; 4) tests and validation; 5) support; 6) stabilization; 7) corrective and evolutionary maintenance; and 8) final project analysis and corrective measures to be applied in future projects. The expected results of the development of such project are related to the integration of the industrial maintenance process, to the continuous monitoring of the machines and to the application of advanced techniques of preventive and proactive maintenance of industrial machines, particularly based on techniques and good practices of the Software Engineering area and on the integration of Cyber-Physical Systems.O potencial desenvolvido pela Indústria 4.0 dotará a indústria nacional de capacidades para desenvolver todo o tipo de procedimentos, especialmente a nível da diferenciação competitiva. As perspetivas e as motivações por detrás da Indústria 4.0 estão relacionadas com uma gestão essencialmente direcionada para a internet industrial, com uma análise integrada e utilização de dados, com a digitalização de produtos e de serviços, com novos modelos disruptivos de negócio e com uma cooperação horizontal no âmbito da cadeia de valor. É através da integração dos sistemas ciber-físicos no processo de manutenção que é possível proceder a um monitoramento contínuo das máquinas, tal como à aplicação de técnicas avançadas para a manutenção preditiva e pró-ativa das mesmas. O presente trabalho é baseado no projeto MANTIS, objetivando, portanto, a construção de uma plataforma específica para a manutenção pró-ativa das máquinas industriais, neste caso em concreto das prensas, que serão as máquinas industriais analisadas ao longo do presente trabalho. Dito de um outro modo, objetiva-se, através de uma plataforma em específico, reduzir todos os custos da sua manutenção, aumentando, portanto, os lucros industriais advindos da produção. Resumidamente, o projeto MANTIS consiste num projeto de investigação multinacional, onde a Unidade de Investigação CISTER desenvolve um papel fundamental, particularmente no fornecimento da infraestrutura de comunicação no Piloto MANTIS. A metodologia adotada é baseada num estudo de acompanhamento, realizado em conjunto com o cliente, e no âmbito da implementação do Piloto da ADIRA. As macro fases que são compreendidas por esta metodologia, e as quais serão seguidas, são: 1) análise detalhada das necessidades de negócio; 2) preparação da especificação da arquitetura; 3) implementação/desenvolvimento; 4) testes e validação; 5) suporte; 6) estabilização; 7) manutenção corretiva e evolutiva; e 8) análise final do projeto e medidas corretivas a aplicar em projetos futuros. Os resultados esperados com o desenvolvimento do projeto estão relacionados com a integração do processo de manutenção industrial, a monitorização contínua das máquinas e a aplicação de técnicas avançadas de manutenção preventiva e pós-ativa das máquinas, especialmente com base em técnicas e boas práticas da área de Engenharia de Software

An End-to-End Big Data Analytics Platform for IoT-enabled Smart Factories: A Case Study of Battery Module Assembly System for Electric Vehicles

Within the concept of factories of the future, big data analytics systems play a critical role in supporting decision-making at various stages across enterprise processes. However, the design and deployment of industry-ready, lightweight, modular, flexible, and low-cost big data analytics solutions remains one of the main challenges towards the Industry 4.0 enabled digital transformation. This paper presents an end-to-end IoT-based big data analytics platform that consists of five interconnected layers and several components for data acquisition, integration, storage, analytics and visualisation purposes. The platform architecture benefits from state-of-the-art technologies and integrates them in a systematic and interoperable way with clear information flows. The developed platform has been deployed in an Electric Vehicle (EV) battery module smart assembly automation system designed by the Automation Systems Group (ASG) at the University of Warwick, UK. The developed proof-of-concept solution demonstrates how a wide variety of tools and methods can be orchestrated to work together aiming to support decision-making and to improve both process and product qualities in smart manufacturing environments

Aggregoiva OPC UA palvelin yleiseen tiedon yhdistämiseen

OPC UA is an industrial communication protocol that enables the modelling of complex information with semantics and exposing it in the address space of an OPC UA server. With developments such as the Industrial Internet of Things and Industrie 4.0, the amount of data in the industrial environment is increasing and it is provided by an increasing number of sources. This can lead to information becoming increasingly scattered, which creates difficulties and inefficiencies in getting a view of all the available information. This thesis presents the design and implementation of a software solution that can integrate information from multiple OPC UA source servers that provide information in different ways and from different viewpoints. An existing aggregating OPC UA server was improved based on elicited requirements to implement an integration platform that can group together and display the heterogeneous information sources in its specially organized address space. The developed software solution consists of three parts: instance aggregation, type aggregation and service mappings, that cooperate together to create the needed functionality. The implemented prototype solution was evaluated in several test cases and found to meet the goals set for it. The instance aggregation procedure is able to find and group relevant information from different sources, while the type aggregation and service mappings keep the type definitions of the aggregated information intact. The instance aggregation procedure can also be configured by the user with a set of rules that enable compatibility with different use case needs. In the future, the results of this thesis will be used as a starting point in the incremental development of improved versions of the aggregation feature.Teollisuudessa käytetty OPC UA -tiedonsiirtomäärittely mahdollistaa monimutkaisen tiedon ja semantiikan esittämisen UPC UA -palvelimen osoiteavaruudessa oliomallin avulla. Teollisen internetin ja Industrie 4.0:n viitoittama suunta teollisuudessa on lisääntyvä tiedon määrä yhä useammista tietolähteistä. Tämän seurauksena tieto voi pirstaloitua ja täten vaikeuttaa kokonaiskuvan saantia olemassaolevasta tiedosta. Tämä diplomityö esittelee suunnittelun ja toteutuksen ohjelmistolle, joka pystyy integroimaan tietoa useista eri OPC UA -lähdepalvelimista, jotka voivat esittää tietoa eri tavoin ja eri näkökulmista. Olemassaolevaa aggregoivaa OPC UA -palvelinta kehitettiin uusiin vaatimuksiin perustuen toteuttamaan integraatioalusta, joka voi ryhmitellä yhteen ja näyttää tietoa erilaisista lähteistä tarkoituksenmukaisesti järjestetyssä nimiavaruudessaan. Kehitetty ohjelmistoratkaisu koostuu kolmesta osasta: instanssien aggregoinnista, tyyppien aggregoinnista ja palvelukartoituksista, jotka toimivat yhdessä tuottaakseen tarvittavan toiminnallisuuden. Kehitettyä prototyyppiratkaisua arvioitiin useissa testitapauksissa ja sen havaittiin täyttävän sille asetetut tavoitteet. Instanssien aggregointi pystyy löytämään ja ryhmittelemään yhteenkuuluvat tiedot eri lähteistä, kun taas tyyppien aggregointi ja palvelukartoitukset pitävät aggregoidun tiedon tyypppimäärittelyt muuttumattomina. Käyttäjä voi konfiguroida instanssien aggregointia käyttämällä erityisiä sääntömäärittelyjä, jotka mahdollistavat aggregointiprosessin yhteensopivuuden eri käyttötarpeiden kanssa. Tulevaisuudessa tässä opinnäytetyössä saatuja tuloksia käytetään lähtökohtana aggregointitoiminnallisuuden asteittaisesssa jatkokehittämisessä