Bearing fault diagnosis method based on dual-channel feature fusion - ZHANG Xiaoning; ZHU Huilong; XIN Liang; YANG Muchen; WANG Hao (机车电传动)
Intelligent diagnosis method based on convolution neural network (CNN) has been widely used in bearing fault diagnosis. However, most existing diagnostic models rely on single-source information inputs, limiting their accuracy and reliability. To solve this limitation, this paper presents a rolling bearing fault diagnosis method based on dual-channel feature fusion. Firstly, the time-frequency analysis diagrams of rolling bearing vibration signals were constructed by using multiple Q-factor continuous Gabor wavelet transform (CMQGWT) and fast spectral coherence (Fast-SC), respectively. Subsequently, a CNN model with dual input channels was constructed, allowing for the fusion of deep time-frequency features extracted from each channel into a new feature at a feature fusion layer. Finally, the diagnosis results were output using a classifier. Through classification and recognition experiments involving single and compound faults in rolling bearings for high-speed trains, compared with the CNN model with a single input channel, the proposed model demonstrates superior diagnostic accuracy and robustness.