Domain correction for hydraulic internal pump leakage detection considering multiclass aberrant flow data

Xirui Chen and Hui Liu

Reliability Engineering and System Safety, 2025, vol. 253, issue C

Abstract: Harsh working environment not only threatens the health of the hydraulic system but also the condition monitoring system. The latter problem will make data aberrant and disable lots of data-based fault detection methods. Inspired by the Fail-Safe principle, the multiclass aberrant data problem is investigated in this study from the perspective of transfer learning. Firstly, the Domain Correction, a variant of Domain Adaptation, is defined theoretically. Then, an indirect Domain Correction framework is proposed and applied to internal pump leakage detection with aberrant flow data. The Teacher-Student structure is the basis. Extra Correction Module is designed to better correct aberrant representation into normal. Layer-wise training and the Noisy Tune are performed to mitigate overfitting. The Self Correction Attention mechanism is presented to help the model focus on the well-measured parts of samples. The proposed method can improve the model's accuracy on the aberrant dataset from 47.1% to 95.0%, meanwhile, the accuracy on the well-measured dataset is guaranteed at 99.2%.

Keywords: Leakage detection; Aberrant data; Domain correction; Knowledge distillation; Correction attention (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:253:y:2025:i:c:s0951832024006112

DOI: 10.1016/j.ress.2024.110539

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