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A wheel set bearing is an important supporting component of a high-speed train. Its quality and performance directly determine the overall safety of the train. Therefore, monitoring a wheel set bearing’s conditions for an early fault diagnosis is vital to ensure the safe operation of high-speed trains. However, the collected signals are often contaminated by environmental noise, transmission path, and signal attenuation because of the complexity of high-speed train systems and poor operation conditions, making it difficult to extract the early fault features of the wheel set bearing accurately. Vibration monitoring is most widely used for bearing fault diagnosis, with the acoustic emission (AE) technology emerging as a powerful tool. This article reports a comparison between vibration and AE technology in terms of their applicability for diagnosing naturally degraded wheel set bearings. In addition, a novel fault diagnosis method based on the optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) and chirp Z-transform (CZT) is proposed to diagnose early composite fault defects in a wheel set bearing. The optimization CYCBD is adopted to enhance the fault-induced impact response and eliminate the interference of environmental noise, transmission path, and signal attenuation. CZT is used to improve the frequency resolution and match the fault features accurately under a limited data length condition. Moreover, the efficiency of the proposed method is verified by the simulated bearing signal and the real datasets. The results show that the proposed method is effective in the detection of wheel set bearing faults compared with the minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) methods. This research is also the first to compare the effectiveness of applying AE and vibration technologies to diagnose a naturally degraded high-speed train bearing, particularly close to actual line operation conditions.

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Axle-box bearings are one of the most critical mechanical components of the high-speed train. Vibration signals collected from axle-box bearings are usually nonlinear and nonstationary, caused by the complicated operating conditions. Due to the high reliability and real-time requirement of axle-box bearing fault diagnosis for high-speed trains, the accuracy and efficiency of the bearing fault diagnosis method based on deep learning needs to be enhanced. To identify the axle-box bearing fault accurately and quickly, a novel approach is proposed in this paper using a simplified shallow information fusion-convolutional neural network (SSIF-CNN). Firstly, the time domain and frequency domain features were extracted from the training samples and testing samples before been inputted into the SSIF-CNN model. Secondly, the feature maps obtained from each hidden layer were transformed into a corresponding feature sequence by the global convolution operation. Finally, those feature sequences obtained from different layers were concatenated into one-dimensional as the fully connected layer to achieve the fault identification task. The experimental results showed that the SSIF-CNN effectively compressed the training time and improved the fault diagnosis accuracy compared with a general CNN.

Despite the numerous studies on bearing fault diagnosis based on frequency domain or time-frequency domain analyses, there is a lack of a fair assessment on which method or methods are practically effective in identifying the fault frequencies of damaged bearings in noisy environments. Most methods were developed based on experiments with simple lab test rigs equipped with bearings having manufactured artificial defects, and the signal-to-noise ratio under lab conditions is too ideal to be useful for verifying the effectiveness of a signal processing method. The purpose of this study is to evaluate the effectiveness of advanced signal processing methods applied in a high-speed train operating environment with multi-source interference. In this work, the most advanced signal processing methods (including spectral kurtosis, deconvolution, and mode decomposition) are studied, and the shortcomings of each method are analyzed. Based on the characteristics of high-speed train wheel set bearings (HSTWSBs), the concept of fault characteristic signal-to-noise ratio (FCSNR) is put forward to quantitatively evaluate the fault periodicity intensity, and corresponding improved methods are proposed by combining the FCSNR with existing signal processing methods; all these methods consider the periodic characteristics and impact characteristics of the bearing fault. The simulation signal and actual signals of HSTWSB with natural defects help verify the effectiveness of the proposed methods. Finally, the advantages and disadvantages of the different signal processing methods are objectively evaluated, and the application scope of each method is analyzed and prospected. This study provides a reference and new ideas for the fault diagnosis of HSTWSB and other industrial bearings.