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Bearing fault diagnosis based on combined multi-scale weighted entropy morphological filtering and bi-LSTM

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Abstract

With the development of industry and technology, mechanical systems’ safety has strong relations with the diagnosis of bearing faults. Accurate fault diagnosis is essential for the safe and stable operation of rotating machinery. Most former research depends too much on the fault signal specificity and learning model’s choices. To overcome the disadvantages of lacking intrinsic mode function (IMF) modal aliasing, low degree of discrimination between data of different fault types, high computational complexity. This paper proposes a method that combines multi-scale weighted entropy morphological filtering (MWEMF) signal processing and bidirectional long-short term memory neural networks (Bi-LSTM). The developed rolling bearing fault diagnosis strategy is then implemented to different databases and potential models to demonstrate the greatly improved system’s ability to reconstruct the time-to-frequency domain characteristics of fault signature signals and reduce learning cost. After verification, the classification accuracy of the proposed model reaches 99%.

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Acknowledgments

This research is supported by Powerchina Equipment Research Institute (Grant No. 2015-ZBY-WT-001). Finally, the author would like to appreciate the editors and reviewers for their valuable comments and constructive suggestions.

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Authors and Affiliations

  1. MEMS Center, Harbin Institute of Technology, Harbin, 150001, China

    Fengqian Zou, Haifeng Zhang, Shengtian Sang, Xiaoming Li, Wanying He & Xiaowei Liu

  2. Key Laboratory of Micro-Systems and Micro-Structures Manufacturing, Ministry of Education, Harbin, 150001, China

    Haifeng Zhang & Xiaowei Liu

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  1. Fengqian Zou
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  2. Haifeng Zhang
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  3. Shengtian Sang
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  4. Xiaoming Li
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  5. Wanying He
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  6. Xiaowei Liu
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Correspondence to Haifeng Zhang or Shengtian Sang.

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Zou, F., Zhang, H., Sang, S. et al. Bearing fault diagnosis based on combined multi-scale weighted entropy morphological filtering and bi-LSTM. Appl Intell 51, 6647–6664 (2021). https://doi.org/10.1007/s10489-021-02229-1

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