An adaptive boundary determination method for empirical wavelet transform and its application in wheelset-bearing fault detection in high-speed trains. (February 2021)

• Record Type: Journal Article
• Authors: Zhang, Qingsong; Ding, Jianming; Zhao, Wentao
• Is Part Of: Measurement. Volume 171(2021)
• Publication Date: 2021-02
• Subjects: Wheelset bearing -- Empirical wavelet transform -- Boundary determination method -- Fault detection; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.108746
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A measurement index for assessing fault information capacity is proposed. An adaptive boundary determination method is proposed. The performance is verified by simulated and actual wheelset bearing signals. Abstract: Wheelset bearing plays a fairly important role in maintaining the safe and stable operation of high-speed trains. Thus, its fault detection is of great significance. Conventional boundary determination methods cannot assess fault information capacity contained in a decomposed band and thus provide a higher number of false boundaries than actually needed. A time-delayed kurtosis-based (TDK) adaptive boundary determination method (TDKABDM) is presented. The proposed TDK, is a measurement index for assessing fault information capacity within a noisy bandwidth. The boundaries determined by TDKABDM are then used to construct Meyer wavelets to extract fault-related resonance bands. The proposed method is validated by fault simulation signals and actual wheelset-bearing vibration signals. The results show that the proposed method can adaptively determine the reasonable boundaries of resonance bands containing fault information. Comparative analyses indicate that the proposed TDKABDM exhibits more excellent performance on extracting wheelset bearing fault characteristics.