High-speed train wheel set bearing fault diagnosis and prognostics: evaluation of signal processing methods under multi-source interference

Abstract

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.

A novel acoustic emission sensor design and modeling method for monitoring the status of high-speed train bearings

“…Moreover, the AE signals from high-speed train bearing damage exhibit unique characteristics, such as low signal-to-noise ratio of early damage signals, strong interference between transmission systems, and non-stationary characteristics due to harsh and time-varying operating conditions. 14,17 These characteristics make the design of AE sensors attractive but challenging.…”

Bandwidth-aware adaptive chirp mode decomposition for railway bearing fault diagnosis

“…1,2 Therefore, condition monitoring is necessary to detect bearing faults in time and thus avoid potential accidents caused by faults. 3–7…”

“…1,2 Therefore, condition monitoring is necessary to detect bearing faults in time and thus avoid potential accidents caused by faults. [3][4][5][6][7] During the rotation process, local defects on the bearing components (e.g., outer race, inner race, or roller) will cause repetitive impulses for the vibration response of the bearing. 8 The impulse repetition frequency known as fault characteristic frequency (FCF) has been widely utilized for bearing fault diagnosis.…”

Early faint fault diagnosis of wheelset axlebox bearings in urban rail trains based on ICiSSA-MOMEDA

“…For example, the complex fault signal transmission path of a wheelset axlebox system leads to a weak fault response. The characteristic frequencies of wheelset axlebox faults are mostly located in the low-frequency band, which is highly susceptible to environmental noise pollution [12][13][14][15]. Therefore, accurate extraction of the early fault features occurring in wheel-to-tube axleboxes through vibration signals has always been a problem that needs to be solved.…”