A rolling bearing fault diagnosis method based on multi-sensor two-stage feature fusion

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (8) : 199-207.

LIU Cang,TONG Jinyu,BAO Jiahan,ZHENG Jinde,PAN Haiyang

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Abstract

To address the problem of insufficient diagnostic accuracy due to the limited information obtained by a single sensor, a two-stage feature fusion fault diagnosis method based on multi-sensor is proposed and applied to the fault diagnosis of rolling bearing under different working conditions. First, the intrinsic mode function (IMF) of each sensor vibration signal is calculated by variational mode decomposition to eliminate redundant information such as noise. Then the time domain, frequency domain and multi-scale entropy features are extracted according to the IMF and fused into a multi-dimensional feature set at the one-dimensional feature level, which is the first stage of feature fusion. Second, an improved deep auto-encoder network based on the Swish activation function and log(cosh) loss function is firstly constructed, then the multi-domain feature set is further fused and fault classification is performed based on the improved deep auto-encoder network, which is the second stage of feature fusion. The proposed model is applied to the rolling bearing fault diagnosis data set under different working conditions. The experimental results show that the proposed method has higher classification accuracy and robustness compared with the existing methods.

Key words

fault diagnosis / auto-encoder network / multi-sensor / feature fusion / rolling bearing

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LIU Cang,TONG Jinyu,BAO Jiahan,ZHENG Jinde,PAN Haiyang. A rolling bearing fault diagnosis method based on multi-sensor two-stage feature fusion[J]. Journal of Vibration and Shock, 2022, 41(8): 199-207

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