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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.11003 |
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| _version_ | 1866911206804029440 |
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| author | Fujiu, Takuma Okazaki, Sho Kaminishi, Kohei Nakata, Yuji Hamamoto, Shota Yokose, Kenshin Hara, Tatsunori Umeda, Yasushi Ota, Jun |
| author_facet | Fujiu, Takuma Okazaki, Sho Kaminishi, Kohei Nakata, Yuji Hamamoto, Shota Yokose, Kenshin Hara, Tatsunori Umeda, Yasushi Ota, Jun |
| contents | In manufacturing systems, identifying the causes of failures is crucial for maintaining and improving production efficiency. In knowledge-based failure-cause inference, it is important that the knowledge base (1) explicitly structures knowledge about the target system and about failures, and (2) contains sufficiently long causal chains of failures. In this study, we constructed Diagnostic Knowledge Ontology and proposed a Function-Behavior-Structure (FBS) model-based maintenance-record accumulation method based on it. Failure-cause inference using the maintenance records accumulated by the proposed method showed better agreement with the set of candidate causes enumerated by experts, especially in difficult cases where the number of related cases is small and the vocabulary used differs. In the future, it will be necessary to develop inference methods tailored to these maintenance records, build a user interface, and carry out validation on larger and more diverse systems. Additionally, this approach leverages the understanding and knowledge of the target in the design phase to support knowledge accumulation and problem solving during the maintenance phase, and it is expected to become a foundation for knowledge sharing across the entire engineering chain in the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11003 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FBS Model-based Maintenance Record Accumulation for Failure-Cause Inference in Manufacturing Systems Fujiu, Takuma Okazaki, Sho Kaminishi, Kohei Nakata, Yuji Hamamoto, Shota Yokose, Kenshin Hara, Tatsunori Umeda, Yasushi Ota, Jun Artificial Intelligence Information Retrieval In manufacturing systems, identifying the causes of failures is crucial for maintaining and improving production efficiency. In knowledge-based failure-cause inference, it is important that the knowledge base (1) explicitly structures knowledge about the target system and about failures, and (2) contains sufficiently long causal chains of failures. In this study, we constructed Diagnostic Knowledge Ontology and proposed a Function-Behavior-Structure (FBS) model-based maintenance-record accumulation method based on it. Failure-cause inference using the maintenance records accumulated by the proposed method showed better agreement with the set of candidate causes enumerated by experts, especially in difficult cases where the number of related cases is small and the vocabulary used differs. In the future, it will be necessary to develop inference methods tailored to these maintenance records, build a user interface, and carry out validation on larger and more diverse systems. Additionally, this approach leverages the understanding and knowledge of the target in the design phase to support knowledge accumulation and problem solving during the maintenance phase, and it is expected to become a foundation for knowledge sharing across the entire engineering chain in the future. |
| title | FBS Model-based Maintenance Record Accumulation for Failure-Cause Inference in Manufacturing Systems |
| topic | Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2510.11003 |