Semi-supervised small sample fault diagnosis under a wide range of speed variation conditions based on uncertainty analysis

Dawei Gao, Kai Huang, Yongsheng Zhu, Linbo Zhu, Ke Yan, Zhijun Ren and C. Guedes Soares

Reliability Engineering and System Safety, 2024, vol. 242, issue C

Abstract: The wide variety of rotational speeds and the small sample dataset make it challenging to mine the fault-related features in the signal, thereby limiting the generalization capability of the model. To address this problem, a semi-supervised method for fault diagnosis through feature perturbation and decision fusion is proposed in this paper. Firstly, a dual correlation model is constructed, and then the model's structural parameters are adjusted to increase the diversity and uncertainty of the diagnostic results. Finally, the conditional probability and penalty term of the sub-models are analyzed based on high-confidence samples, enabling the achievement of the final fusion diagnosis. To verify the effectiveness of the proposed method, variable speed experiments containing 20 kinds of speeds were conducted based on the Bearing Prognostics Simulator test bench. The validity of the proposed method is demonstrated through the constructed dataset.

Keywords: Small sample fault diagnosis; Variable speed; Decision fusion; Semi-supervised learning (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:242:y:2024:i:c:s0951832023006609

DOI: 10.1016/j.ress.2023.109746

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