Text mining for AI enhanced failure detection and availability optimization in production systems

Ansari, Fazel; Kohl, Linus; Giner, Jakob; Meier, Horst

doi:10.1016/j.cirp.2021.04.045

Cited by 15 publications

(5 citation statements)

References 9 publications

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“…This requires focusing on preparing and selecting the data, which consumes considerable time and resources in real world use cases and industrial projects. Therefore, only with appropriate pre-processing steps can the developed AI algorithm achieve optimal results with the selected data (Ansari et al 2021).…”

Section: Functional Capabilities For Ai-enhanced Assistance Systemsmentioning

confidence: 99%

“…The chosen data analysis unit depends on the data source and data type, which results in the use of different algorithms. In the case of structured data, ML methods are used to perform analysis such as downtime prediction (Ansari et al 2021) and detection of critical machine failures (Passath et al 2021) or anomalies, depending on the requirements. For unstructured data, TM models are used to extract e.g.…”

Section: Fu3 -Data Analysismentioning

confidence: 99%

“…In the use-case of maintenance in the automotive industry, an AI-enhanced methodology for a digital shift book has been developed. The goal of the Proof of Concept (PoC) application was to increase the OEE by providing accurate downtime prediction and assistance in documentation (Ansari et al 2021). In the usecase the FU1-3 and FU6 have been implemented and validated.…”

Section: Automotive Industrymentioning

confidence: 99%

“…The automotive manufacturer's data spans three years and includes Rough Problem Level, Fine Problem Level, Defective part, Problem and, Downtime. The data acquisition unit was implemented using structured and unstructured data from maintenance reports (Ansari et al 2021). In the pre-processing unit, as described in detail in Ansari et al (2021), structured data was normalized, and outliers were removed.…”

Section: Automotive Industrymentioning

confidence: 99%

“…The data acquisition unit was implemented using structured and unstructured data from maintenance reports (Ansari et al 2021). In the pre-processing unit, as described in detail in Ansari et al (2021), structured data was normalized, and outliers were removed. In the case of the unstructured data, among other things, stop words were removed and lemmatization was performed to extract nouns and verbs.…”

Section: Automotive Industrymentioning

confidence: 99%

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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.

Kohl¹,

Ansari²,

Sihn³

2021

Competence Development and Learning Assistance Systems for the Data-Driven Future

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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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Section: Functional Capabilities For Ai-enhanced Assistance Systemsmentioning

confidence: 99%

Section: Fu3 -Data Analysismentioning

confidence: 99%

Section: Automotive Industrymentioning

confidence: 99%

Section: Automotive Industrymentioning

confidence: 99%

Section: Automotive Industrymentioning

confidence: 99%

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