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A semi-supervised transferable LSTM with feature evaluation for fault diagnosis of rotating machinery

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Abstract

Aiming at the issue of impracticality or costliness of collecting enough labeled signals under all working conditions, the performance of a method usually suffers a significant loss when the model trained on one working condition is directly applied to another working condition. Thus, the entropy gain ratio combined with a semi-supervised transferable LSTM (EGR-STLSTM) network is established for fault diagnosis of rotating machinery under variable working conditions. First, the ERG is devoted to evaluating the multi-domain features extracted based on the characteristics of rotating machinery. Then, the optimal feature subset is fed into the STLSTM to obtain a pre-trained network. Finally, a semi-supervised transfer learning strategy, that is, with the aid of a small number of target labeled samples, is applied to the pre-trained model to achieve competitive performance in target tasks. The proposed EGR-STLSTM network fully uses the vibration characteristics of rotating machinery and retains the specialized knowledge derived from the source domain. The fault diagnosis method is verified with raw vibration signals from a bearing test rig and a gearbox test rig. The experimental results indicate that compared with the well-known methods, the proposed method significantly improves the diagnostic performance by reusing the pre-trained model and reduces the demand for massive labeled samples under variable working conditions.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant Nos. 51975067), National Key Research and Development Program (2018YFB2001400), and Fundamental Research Funds for the Central Universities (2020CDCGJX022). Here, the authors express their heartfelt thanks to Ms. Zhou Peng, Chongqing university for her valuable suggestions for revising the draft. Meanwhile, the authors would like to thank NCEPU for providing the gear test platform.

CRediT authorship contribution statement

Zhi Tang: Writing-original draft, Methodology. Lin Bo: Conceptualization, Supervision. Xiaofeng Liu: Funding acquisition, Writing-review & editing, Daiping Wei: Writing-review & editing.

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Authors and Affiliations

  1. State Key Laboratory of Mechanical Transmissions, Chongqing University, 400044, Chongqing, People’s Republic of China

    Zhi Tang, Lin Bo, Xiaofeng Liu & Daiping Wei

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  1. Zhi Tang
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  2. Lin Bo
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  3. Xiaofeng Liu
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Correspondence to Lin Bo.

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Tang, Z., Bo, L., Liu, X. et al. A semi-supervised transferable LSTM with feature evaluation for fault diagnosis of rotating machinery. Appl Intell 52, 1703–1717 (2022). https://doi.org/10.1007/s10489-021-02504-1

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