Inter-shaft fault diagnosis method based on deep extreme learning machine optimized with dung beetle optimizer

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (21) : 96-106.

LUAN Xiaochi, TANG Jiezhong, SHA Yundong

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Abstract

For the problems that the fault signal transmission path of inter-shaft bearing is complex, interfered by background noise, fault features extraction are difficult, and the accuracy of traditional diagnosis model is limited by the location of measuring points, an inter-shaft bearing fault diagnosis method based on a combination of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Deep Extreme Learning Machine (DELM) optimized by Dung Beetle Optimizer (DBO) was proposed. Firstly, the original signal was decomposed,screened and reconstructed by CEEMDAN and the intrinsic mode function screening criteria composed of energy ratio-correlation coefficient-kurtosis value,and features were extracted from the time domain and frequency domain of the reconstructed signal to form the feature matrix. Secondly, reconstruct the DELM after optimizing the initial weight of DELM model using the diagnostic accuracy as the fitness value of DBO. Finally, the feature matrix was input into DELM to complete fault diagnosis. Taking the inter-shaft bearing fault data as an example, the DELM diagnosis accuracy after DBO optimization has been greatly improved, and the diagnosis accuracy was still up to 98.75% in the 45° direction which was difficult to diagnose. The results show that the diagnostic method can effectively identify the inter-shaft bearing fault type and shows strong robustness and generalization ability.

Key words

Inter-shaft / Fault diagnosis / Mode decomposition / Dung beetle optimizer(DBO) / Deep extreme learning machine(DELM)

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LUAN Xiaochi, TANG Jiezhong, SHA Yundong. Inter-shaft fault diagnosis method based on deep extreme learning machine optimized with dung beetle optimizer[J]. Journal of Vibration and Shock, 2024, 43(21): 96-106

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