Open AccessA CNN Based Approach with Identity Mapping Module for Mechanical Fault Diagnosis of High Voltage Circuit Breaker
Author(s) -
Ke Zhou,
Xiangyu Lin,
Xiaoming Wang,
Wei Zhou,
Wen-Wei Li
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 - 10.1088/1742-6596/1601/6/062048
Subject(s) - fault (geology) , circuit breaker , convolutional neural network , preprocessor , computer science , pattern recognition (psychology) , convolution (computer science) , artificial intelligence , identity (music) , voltage , generalization , electronic engineering , artificial neural network , engineering , electrical engineering , mathematics , acoustics , seismology , geology , mathematical analysis , physics
In order to improve the accuracy and generalization of mechanical fault diagnosis of high voltage circuit breaker, a CNN with identity mapping module based approach is proposed. Six acceleration sensors are installed at specific positions of the circuit breaker to collect comprehensive vibration signals. A mechanical fault diagnosis model is established based on convolution neural network with identity mapping module. After preprocessing such as down sampling and data splicing, the input signals are analyzed to extract feature information and identify mechanical fault. The experimental results show that the proposed method has better performance in mechanical fault detection compared with traditional CNN method.