Multi-modal adaptive feature extraction for early-stage weak fault diagnosis in bearings

Zhenzhong Xu; Xu Chen; Linchao Yang; Jiangtao Xu; Shenghan Zhou; Zhenzhong Xu; Xu Chen; Linchao Yang; Jiangtao Xu; Shenghan Zhou

doi:10.3934/era.2024183

Electronic Research Archive

2024, Volume 32, Issue 6: 4074-4095. doi: 10.3934/era.2024183

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Multi-modal adaptive feature extraction for early-stage weak fault diagnosis in bearings

1.
College of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001, China
2.
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
3.
School of Economics and Management, North China Electric Power University, Beijing 102206, China
† These two authors contributed equally to this work

Received: 01 April 2024 Revised: 04 June 2024 Accepted: 18 June 2024 Published: 25 June 2024

We present a novel multi-modal adaptive feature extraction algorithm considering both time-domain and frequency-domain modalities (AFETF), coupled with a Bidirectional Long Short-Term Memory (Bi-LSTM) network based on the Grey Wolf Optimizer (GWO) for early-stage weak fault diagnosis in bearings. Singular Value Decomposition (SVD) was employed for noise reduction, while Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was utilized for signal decomposition, facilitating further signal processing. AFETF algorithm proposed in this paper was employed to extract weak fault features. The adaptive diagnostic process was further enhanced using Bi-LSTM network optimized with GWO, ensuring objectivity in the hyperparameter optimization. The proposed method was validated for datasets containing weak faults with a 0.2 mm crack and strong faults with a 0.4 mm crack, demonstrating its effectiveness in early-stage fault detection.
- bearings,
- weak fault,
- time domain,
- frequency domain,
- AFETF
Citation: Zhenzhong Xu, Xu Chen, Linchao Yang, Jiangtao Xu, Shenghan Zhou. Multi-modal adaptive feature extraction for early-stage weak fault diagnosis in bearings[J]. Electronic Research Archive, 2024, 32(6): 4074-4095. doi: 10.3934/era.2024183

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Abstract

We present a novel multi-modal adaptive feature extraction algorithm considering both time-domain and frequency-domain modalities (AFETF), coupled with a Bidirectional Long Short-Term Memory (Bi-LSTM) network based on the Grey Wolf Optimizer (GWO) for early-stage weak fault diagnosis in bearings. Singular Value Decomposition (SVD) was employed for noise reduction, while Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was utilized for signal decomposition, facilitating further signal processing. AFETF algorithm proposed in this paper was employed to extract weak fault features. The adaptive diagnostic process was further enhanced using Bi-LSTM network optimized with GWO, ensuring objectivity in the hyperparameter optimization. The proposed method was validated for datasets containing weak faults with a 0.2 mm crack and strong faults with a 0.4 mm crack, demonstrating its effectiveness in early-stage fault detection.

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