Schriftenreihe der Wissenschaftlichen Gesellschaft für Arbeits- und Betriebsorganisation (WGAB) e.V., 2021, 229-242
A Modular Federated Learning Architecture for Integration of AI-enhanced Assistance in Industrial Maintenance - A novel architecture for enhancing industrial maintenance management systems in the automotive and semiconductor industry.

Bibtex

@Article{,
 year    = "2021", 
   
  journal    = "Schriftenreihe der Wissenschaftlichen Gesellschaft für Arbeits- und Betriebsorganisation (WGAB) e.V.", 
  pages    = "229-242", 
  title    = "A Modular Federated Learning Architecture for Integration of AI-enhanced Assistance in Industrial Maintenance - A novel architecture for enhancing industrial maintenance management systems in the automotive and semiconductor industry. ", 
  author    = "Univ.-Ass. Dipl.-Ing. Linus Kohl 
Research Group Smart and Knowledge Based Maintenance Institute of Management Science
TU Wien 
Fraunhofer Austria Research GmbH Vienna 
Priv.-Doz. Dr.-Ing. Fazel Ansari 
Research Group Smart and Knowledge Based Maintenance Institute of Management Science
TU Wien 
Fraunhofer Austria Research GmbH Vienna 
Univ.-Prof. Dr.-Ing. Dipl. Wirtsch.-Ing. Prof. eh. Dr. h.c. Wilfried Sihn 
Institute of Management Science TU Wien 
Fraunhofer Austria Research GmbH Vienna 
", 
  doi    = "https://doi.org/10.30844/wgab_2021_14", 
  abstract    = "Artificial Intelligence (AI) plays an increasingly important role for the implementation and failure-free operation of Cyber-Physical Production Systems (CPPS). Recent market studies show that investment in AI-enhanced maintenance is increasing as one of the most important use cases of Industry 4.0. AI systems enable the improvement of various Key Performance Indicators (KPI), ultimately leading to a reduction in costs and optimizing plant management in smart factories. At the same time, manufacturing enterprises in diverse sectors have very high expectations from any kind of AI solution comparing to conventional solutions. Today manufacturing enterprises use only a quarter of their data and therefore leave an enormous, untapped potential. The use of Text Mining (TM) realizes the untapped value of existing unstructured or semi-structured textual data. 
This paper presents a transferable and scalable architecture for a cognitive maintenance system of a human-centered assistance system that enables holistic sensing of the environment by using physical and virtual sensors. By focusing on generalizability, scalability, adaptability, reliability, and user acceptance, a novel architecture for cognitive maintenance system is proposed. The so called ARCHIE, Architecture for a Cognitive Maintenance System, addresses common challenges in the application of AI systems in the industrial environment.
Human-centered cognitive systems aim to automate manufacturing processes and assist workers in their cognitive tasks. This can be achieved by using the untapped potential of combining unstructured and structured data in order to extract hidden knowledge. ARCHIE aims at realizing an AI-enhanced approach for a human-centered assistance system. ARCHIE incorporates physical and virtual sensors that capture machine states, parameters, human knowledge, and skills to optimize relevant KPIs. This includes a reduction in documentation time, Mean Time Between Failures (MTBF) and Mean Failure Detection Time (MFDT), as well as an increase in uptime, leading ultimately to an improved Overall Equipment Efficiency (OEE). These improvements are enabled by the combined use of AI in the form of TM, Federated Learning and Knowledge Graphs. In the presented use-case from the automotive industry, a reduction in MFDT below 60min by 97.3% and an increase in OEE by 5.3% was achieved. In the Semiconductor industry, the partial application of ARCHIE allows the querying of competence distributions based on a given maintenance task, enabling automated allocation of maintenance technicians and trend analyses. Generalizability, scalability, adaptability, reliability, and user acceptance were also evaluated in the use cases presented.
", 
  
}

Abstract

Abstract

Artificial Intelligence (AI) plays an increasingly important role for the implementation and failure-free operation of Cyber-Physical Production Systems (CPPS). Recent market studies show that investment in AI-enhanced maintenance is increasing as one of the most important use cases of Industry 4.0. AI systems enable the improvement of various Key Performance Indicators (KPI), ultimately leading to a reduction in costs and optimizing plant management in smart factories. At the same time, manufacturing enterprises in diverse sectors have very high expectations from any kind of AI solution comparing to conventional solutions. Today manufacturing enterprises use only a quarter of their data and therefore leave an enormous, untapped potential. The use of Text Mining (TM) realizes the untapped value of existing unstructured or semi-structured textual data. This paper presents a transferable and scalable architecture for a cognitive maintenance system of a human-centered assistance system that enables holistic sensing of the environment by using physical and virtual sensors. By focusing on generalizability, scalability, adaptability, reliability, and user acceptance, a novel architecture for cognitive maintenance system is proposed. The so called ARCHIE, Architecture for a Cognitive Maintenance System, addresses common challenges in the application of AI systems in the industrial environment. Human-centered cognitive systems aim to automate manufacturing processes and assist workers in their cognitive tasks. This can be achieved by using the untapped potential of combining unstructured and structured data in order to extract hidden knowledge. ARCHIE aims at realizing an AI-enhanced approach for a human-centered assistance system. ARCHIE incorporates physical and virtual sensors that capture machine states, parameters, human knowledge, and skills to optimize relevant KPIs. This includes a reduction in documentation time, Mean Time Between Failures (MTBF) and Mean Failure Detection Time (MFDT), as well as an increase in uptime, leading ultimately to an improved Overall Equipment Efficiency (OEE). These improvements are enabled by the combined use of AI in the form of TM, Federated Learning and Knowledge Graphs. In the presented use-case from the automotive industry, a reduction in MFDT below 60min by 97.3% and an increase in OEE by 5.3% was achieved. In the Semiconductor industry, the partial application of ARCHIE allows the querying of competence distributions based on a given maintenance task, enabling automated allocation of maintenance technicians and trend analyses. Generalizability, scalability, adaptability, reliability, and user acceptance were also evaluated in the use cases presented.

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