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.
Acoustic emission (AE) technology is suitable for monitoring the status of high-speed train bearings owing to its high sensitivity and real-time dynamic monitoring capabilities. However, a complete theoretical model of the AE sensor, which is the core component of AE signal sensing equipment, has not yet been reported. The existing matching layer models do not account for wave attenuation in the matching layer. To address these shortcomings, we established a novel piezoelectric AE sensor design and modeling method. First, a rough contact model is established for piezoelectric ceramics, and the influence of roughness on size selection of piezoelectric ceramics is analyzed. Second, the sound intensity transmission coefficient (SITC) model of the matching layer is established considering the attenuation of AE waves, and the corresponding relationship between the attenuation coefficient and the optimum thickness of the matching layer is derived. Then a complete finite element (FE) model of an AE sensor is established, and the electroacoustic properties of the AE sensor are numerically simulated based on acoustic and piezoelectric coupling. Furthermore, AE sensors with different thicknesses of matching layers are constructed, and the validity of the mathematical model of the matching layer is verified through a lead-breaking experiment. Thereafter, a novel comprehensive performance evaluation index (CPEI) is designed through principal component analysis (PCA) based on hit parameters of AE sensors. Finally, the effectiveness and environmental adaptability of the AE sensor is verified by performance testing under complex conditions near an actual high-speed train line. The proposed method can provide a valuable theoretical framework for AE sensor design and status monitoring of high-speed train bearings.
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