Bearing fault diagnosis method based on deep learning of multi-domain information fusion

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (23) : 47-55.

GE Zhuo1,2, XIA Huameng1, WANG Kailiang1, XU Zengbing3, DING Gaige3

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Abstract

Aiming at the problem of a single vibration signal containing fault information being easily hidden and the weak diagnostic ability of a single deep learning model leading to low accuracy in bearing fault diagnosis, a deep learning fault diagnosis method based on multi-domain information fusion is proposed in this paper. Variational Mode Decomposition (VMD) method is adopted to decompose the original vibration signal into multiple IMF components, while fast Fourier transformation FFT transforming each IMF component into frequency domain samples. After that, multiple IMF components and their corresponding frequency domain samples are inputted into multiple deep metric learning (DML) models and deep belief network DBN models for preliminary diagnostic analysis, respectively. And then a simple soft voting method is used to fuse these preliminary diagnostic results to obtain the final diagnostic result. Finally, through the analysis of bearing fault diagnosis experiments, the results show that the proposed method not only has good diagnostic performance, but also outperforms information fusion diagnosis methods based on time domain and frequency domain, respectively.

Key words

Information fusion / Deep metric learning / deep belief network DBN / Soft voting method

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GE Zhuo1, 2, XIA Huameng1, WANG Kailiang1, XU Zengbing3, DING Gaige3. Bearing fault diagnosis method based on deep learning of multi-domain information fusion[J]. Journal of Vibration and Shock, 2024, 43(23): 47-55

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