A case-learning-based paradigm for quantitative recommendation of fault diagnosis algorithms: A case study of gearbox

Xinyu Zou, Laifa Tao, Lulu Sun, Chao Wang, Jian Ma and Chen Lu

Reliability Engineering and System Safety, 2023, vol. 237, issue C

Abstract: Prognostics and Health Management (PHM) is a core technology for condition-based maintenance. The diversity of PHM algorithms and the complexity of design factors make it challenging to choose an appropriate algorithm for a specific application. Consequently, automatic PHM algorithm recommendation (PHM-AR) is of great significance for PHM developers to implement rapid design of PHM systems. However, the two existing paradigms for PHM-AR rely heavily on expert experience and failure data respectively. In this paper, we propose a new third paradigm, called the case-learning-based paradigm, for quantitative recommendation of fault diagnosis algorithms, focusing on diagnosis task attribute analysis, multi-dimensional feature representation, and recommendation model construction. Specifically, to provide a quantitative basis for the recommendation of diagnosis algorithms, we define a three-level attribute set of diagnosis tasks and propose a quantitative representation method to make data structured. Then, a recommendation model based on the Classification and Regression Tree (CART) is proposed. Finally, taking gearbox as an example, we construct a diagnosis case set and verify the effectiveness of the proposed framework. Experimental results show that the average recommendation accuracy of our proposed method reaches 70.40%. These results also elucidate that the proposed method can learn the applicable rules of various diagnosis algorithms.

Keywords: Case-learning-based paradigm; Diagnosis algorithm recommendation; Multi-dimensional feature representation; Task attribute analysis (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:237:y:2023:i:c:s0951832023002867

DOI: 10.1016/j.ress.2023.109372

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