Open AccessResearch on rolling bearing fault diagnosis of high-speed train
Author(s) -
Wei Zhuo,
Yu Zhang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1314/1/012086
Subject(s) - bearing (navigation) , train , fault (geology) , artificial neural network , wavelet , network packet , wavelet packet decomposition , computer science , artificial intelligence , engineering , real time computing , pattern recognition (psychology) , wavelet transform , geology , computer network , cartography , seismology , geography
The rolling bearing is the main vulnerable component of a rotating machinery on high-speed train, which is very important to the safe operation of high-speed train. Based on the fundamental analysis of the causes of rolling bearing faults on high-speed trains, the feasibility of wavelet packet analysis and BP neural network in rolling bearing fault diagnosis of high-speed trains is discussed. In this paper, wavelet packet is used to denoise data and extract features, then the training BP neural network is used to diagnose and analyze the characteristic data accurately, and the diagnosis results are obtained. By comparing the training results, the number of wavelet packet decomposition is determined, and the accuracy of BP neural network diagnosis is improved. In this paper, the analysis is carried out by using the bearing data of American electrical laboratory “Case Western Reserve University”, an efficient and fast method for rolling bearing diagnosis is presented, which can be applied to real-time fault diagnosis and monitoring of rolling bearing faults for high speed train.