Open AccessBearing Intelligent Fault Diagnosis Based on Convolutional Neural Networks
Author(s) -
An J,
Peng An
Publication year - 2022
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.156
H-Index - 13
ISSN - 1998-4464
DOI - 10.46300/9106.2022.16.57
Subject(s) - convolutional neural network , computer science , benchmark (surveying) , fault (geology) , feature extraction , artificial intelligence , bearing (navigation) , pattern recognition (psychology) , process (computing) , artificial neural network , feature selection , dimension (graph theory) , identification (biology) , feature (linguistics) , data mining , mathematics , linguistics , philosophy , botany , geodesy , seismology , geology , pure mathematics , biology , geography , operating system
The traditional intelligent identification method requires a complex feature extraction process and much diagnosis experience, considering the characteristics of one dimension of bearing vibration signals, a new method of intelligent fault diagnosis based on 1-dimensional convolutional neural network is presented. This method automatically extracts features from frequency domain signals and avoids artificial feature selection and feature extraction. The proposed method is validated on bearing benchmark datasets, these datasets are collected in different fault location, different health conditions and different operating conditions. The result shows that the proposed method can not only adaptively obtain representative fault features from the datasets, but also achieve higher diagnosis accuracy than the existing methods.