Open AccessConstruction of Fault Diagnosis Model of Metro Wheel Speed Box System Based on Convolution Neural Network
Author(s) -
Xiangyi Ren,
Jingyuan Wu
Publication year - 2020
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 - 10.1088/1742-6596/1650/3/032128
Subject(s) - fault (geology) , convolutional neural network , signal (programming language) , vibration , artificial neural network , convolution (computer science) , computer science , process (computing) , mechanism (biology) , deep learning , artificial intelligence , engineering , pattern recognition (psychology) , real time computing , acoustics , philosophy , physics , epistemology , seismology , programming language , geology , operating system
This research focuses on a fault detection method apply on the subway wheelsets. This fault diagnosis method is mainly aimed at the vibration signal to distinguish the fault and abnormal situation. At the same time, the vibration signal incorporated with the deep learning method to establish the diagnosis mechanism. Reliable online data of subway in a city of China and CNN (Convolutional Neural Network) are applied in the web training process. The degradation of vibration signal of rolling functional unit (wheelset) was summarized, and the difference between normal and fault signals of rolling functional unit (wheelset) was studied. The feedback learning mechanism makes it possible to update the neural network in real time.