A maturity framework for data driven maintenance
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914889695494144 |
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| author | Rijsdijk, Chris van de Wijnckel, Mike Tinga, Tiedo |
| author_facet | Rijsdijk, Chris van de Wijnckel, Mike Tinga, Tiedo |
| contents | Maintenance decisions range from the simple detection of faults to ultimately predicting future failures and solving the problem. These traditionally human decisions are nowadays increasingly supported by data and the ultimate aim is to make them autonomous. This paper explores the challenges encountered in data driven maintenance, and proposes to consider four aspects in a maturity framework: data / decision maturity, the translation from the real world to data, the computability of decisions (using models) and the causality in the obtained relations. After a discussion of the theoretical concepts involved, the exploration continues by considering a practical fault detection and identification problem. Two approaches, i.e. experience based and model based, are compared and discussed in terms of the four aspects in the maturity framework. It is observed that both approaches yield the same decisions, but still differ in the assignment of causality. This confirms that a maturity assessment not only concerns the type of decision, but should also include the other proposed aspects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18996 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A maturity framework for data driven maintenance Rijsdijk, Chris van de Wijnckel, Mike Tinga, Tiedo Artificial Intelligence Machine Learning Systems and Control E.1; F.2; G.3; I.2.8; I.6.4; J.6 Maintenance decisions range from the simple detection of faults to ultimately predicting future failures and solving the problem. These traditionally human decisions are nowadays increasingly supported by data and the ultimate aim is to make them autonomous. This paper explores the challenges encountered in data driven maintenance, and proposes to consider four aspects in a maturity framework: data / decision maturity, the translation from the real world to data, the computability of decisions (using models) and the causality in the obtained relations. After a discussion of the theoretical concepts involved, the exploration continues by considering a practical fault detection and identification problem. Two approaches, i.e. experience based and model based, are compared and discussed in terms of the four aspects in the maturity framework. It is observed that both approaches yield the same decisions, but still differ in the assignment of causality. This confirms that a maturity assessment not only concerns the type of decision, but should also include the other proposed aspects. |
| title | A maturity framework for data driven maintenance |
| topic | Artificial Intelligence Machine Learning Systems and Control E.1; F.2; G.3; I.2.8; I.6.4; J.6 |
| url | https://arxiv.org/abs/2407.18996 |