High-speed train wheel set bearing fault diagnosis and prognostics: Fingerprint feature recognition method based on acoustic emission

Abstract

Acoustic emission (AE) technology is suitable for the condition monitoring and fault diagnosis of high-speed train wheel set bearings owing to its high frequency and high sensitivity [1]. However, current AE diagnosis methods cannot consider both real-time characteristic and fault periodicity. To overcome these shortcomings, a fingerprint feature recognition method is proposed in this paper. First, the concept of dynamic threshold is proposed to ensure that the typical burst or hit-based AE signal can be accurately extracted under different speeds, loads, and damaged bearing states. Based on the dynamic threshold, a specific feature, namely the fingerprint feature, is defined to provide an instant visual pattern of the bearing fault. Second, a clustering significance index (CSI) is constructed, which can not only guide the intelligent selection of the dynamic threshold, but also help to realize the quantitative evaluation of the bearing damage state. Furthermore, this study combines hit statistics with the fault frequency to form a fault hit statistical spectrum. On this basis, a fault hit significance index (FHSI) is established for the quantitative judgment of the bearing damage state. Finally, the validity of the proposed methods was verified by testing under complex test conditions close to the actual line of a high-speed train, providing a valuable reference for the online monitoring of the bearing state under actual industrial conditions.