Automatic detection of a wheelset bearing fault using a multi-level empirical wavelet transform. (February 2019) - Ding, Jianming; Ding, Chengcheng
Highlights: A novel automatic fault detection method, named AFDMLEWT, is proposed. MLEWT for extracting the sidebands with different bandwidths, is proposed. ESKDB is designed for evaluating information capacity in the different bandwidths. SSESKDB is developed for selecting segments of MLEWT containing fault information. Abstract: A wheelset bearing is one of the crucial mechanical parts in a high-speed train. Its fault detection is of great significance, as it ensures the safety of a high-speed train service. To resolve the boundary changeability of the sidebands and false segments caused by measured noises and vibrational interferences, a novel automatic fault detection method of the wheelset bearing, named AFDMLEWT, is proposed based on the proposed multi-level empirical wavelet transform (MLEWT) and the developed segment selection method, named SSESKDB. MLEWT provides a complete framework for extracting the sidebands with different bandwidths distributed throughout different frequency positions. A novel index for evaluating the fault information capacity contained in different decomposition bandwidths, named ESKDB, is proposed. The segment selection method based on ESKDB, named SSESKDB, is proposed to select those segments of MLEWT containing the fruitful fault information in the full frequency range of wheelset bearing fault detection. Finally, the proposed method is then validated using two simulated signals and bench tests.