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Adaptive feature mode decomposition: a fault-oriented vibration signal decomposition method for identification of multiple localized faults in rotating machinery

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Abstract

Identification of multiple mechanical faults from vibration signals has always been one of the most challenging tasks in the field of condition monitoring and fault diagnosis. This study proposes a new fault-oriented vibration signal decomposition method, called adaptive feature mode decomposition (AFMD), to identify multiple localized faults in rotating machines interfered by strong periodic harmonics in a robust and effective manner. The autoregressive model is first introduced as a preprocessing technique for initially reducing deterministic components of the raw signal. Then, for signal decomposition, an adaptive finite impulse response (FIR) filter bank is designed utilizing the blind deconvolution theory. The filter coefficients of the FIR filter bank are iteratively updated to make each filtered sub-signal infinitely approach their deconvolution objective functions based on the correlated kurtosis. Meanwhile, the filter length is adaptively determined using the developed evaluation indicator termed as the weighted squared envelope harmonic-to-noise ratio. Finally, several decomposed modes focusing on fault signatures can be acquired automatically, using the newly proposed mode selection strategy that considers signal similarity across multiple domains. The proposed AFMD method can significantly reduce the likelihood of incorrect diagnoses, as demonstrated by two simulated and two experimental datasets with multiple localized bearing and gear faults. The analysis results show that the proposed method outperforms over the state-of-the-art feature mode decomposition and the most popular variational mode decomposition in multi-fault feature extraction and weak fault detection, when interfered by strong periodic harmonics as well as other background noise.

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Data availability

Data of this study will be made available from the corresponding author on reasonable request.

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Acknowledgements

This work is supported by the Project funded by China Postdoctoral Science Foundation (2022M721312), Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems (GZKF-202206), the Department of Science and Technology of Jilin Province (20230508154RC), the Education Department of Jilin Province (JJKH20231149KJ) and the National Natural Science Foundation of China (U21A20137, 52005212). The authors would like to thank Dr. Yonghao Miao for sharing the MATLAB code and thank Prof. Yaguo Lei for providing the XJTU-SY bearing datasets for public.

Funding

This work is funded by China Postdoctoral Science Foundation (Grant No. 2022M721312), State Key Laboratory of Fluid Power and Mechatronic Systems (Grant No. GZKF-202206), National Natural Science Foundation of China (Grant Nos. U21A20137, 52005212), Department of Science and Technology of Jilin Province (Grant No. 20230508154RC) and Education Department of Jilin Province (Grant No. JJKH20231149KJ).

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Authors and Affiliations

  1. School of Mechanical and Aerospace Engineering, Jilin University, Changchun, 130025, China

    Xiuzhi He, Xiaoqin Zhou, Jieli Li, Rongqi Wang, Guofeng Yao & Qiang Liu

  2. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China

    Xiuzhi He

  3. Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, Changchun, 130025, China

    Xiuzhi He, Xiaoqin Zhou, Jieli Li, Rongqi Wang, Guofeng Yao & Qiang Liu

  4. Department of Mechanical and Materials Engineering, Queen’s University, Kingston, K7L 3N6, Canada

    Chris K. Mechefske

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  1. Xiuzhi He
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Correspondence to Qiang Liu.

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He, X., Zhou, X., Li, J. et al. Adaptive feature mode decomposition: a fault-oriented vibration signal decomposition method for identification of multiple localized faults in rotating machinery. Nonlinear Dyn 111, 16237–16270 (2023). https://doi.org/10.1007/s11071-023-08703-4

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