Bearings are key components of modern mechanical equipment. To address the issue that the limited information contained in the single-source signal of the bearing leads to the limited accuracy of the single-source fault diagnosis method, a multi-sensor fusion fault diagnosis method is proposed to improve the reliability of bearing fault diagnosis. Firstly, the feature extraction process of the convolutional neural network (CNN) is improved based on the theory of variational Bayesian inference, which forms the variational Bayesian inference convolutional neural network (VBICNN). VBICNN is used to obtain preliminary diagnosis results of single-channel signals. Secondly, considering the redundancy of information contained in multi-channel signals, a voting strategy is used to fuse the preliminary diagnosis results of the single-channel model to obtain the final results. Finally, the proposed method is evaluated by an experimental dataset of the axlebox bearing of a high-speed train. The results show that the average diagnosis accuracy of the proposed method can reach more than 99% and has favorable stability.

The operating state of axle box bearings is crucial to the safety of high-speed trains, and the vibration acceleration signal is a commonly used bearing-health-state monitoring signal. In order to extract hidden characteristic frequency information from the vibration acceleration signal of axle box bearings for fault diagnosis, a method for extracting the fault characteristic frequency based on principal component analysis (PCA) fusion and the optimal bandpass filtered denoising signal analytic energy operator (AEO) demodulation spectrum is proposed in this paper. PCA is used to measure the dimension reduction and fusion of three-direction vibration acceleration, reducing the interference of irrelevant noise components. A new type of multi-channel bandpass filter bank is constructed to obtain filtering signals in different frequency intervals. A new, improved average kurtosis index is used to select the optimal filtering signals for different channel filters in a bandpass filter bank. A dimensionless characteristic index characteristic frequency energy concentration coefficient (CFECC) is proposed for the first time to describe the energy prominence ability of characteristic frequency in the spectrum and can be used to determine the bearing fault type. The effectiveness and applicability of the proposed method are verified using the simulation signals and experimental signals of four fault bearing test cases. The results demonstrate the effectiveness of the proposed method for fault diagnosis and its advantages over other methods.