Fault Diagnosis Method for Bearing Based on Attention Mechanism and Multi-Scale Convolutional Neural Network
Author:
Affiliation:
1. Jinzhong College of Information, Jinzhong, China
2. Taiyuan Institute of Technology, Taiyuan, China
Funder
Shanxi Province Higher Education Teaching Reform and Innovation Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6287639/10380310/10411892.pdf?arnumber=10411892
Reference39 articles.
1. Bearing Fault Diagnosis Based on Iterative 1.5-Dimensional Spectral Kurtosis
2. Analysis of Statistical Time-Domain Features Effectiveness in Identification of Bearing Faults From Vibration Signal
3. Research of Fault Diagnosis of Mine Rolling Bearing Based on Time-Frequency Analysis
4. A Novel Approach To Diagnosis Of Analog Circuit Incipient Faults Based On KECA And OAO LSSVM
5. An Analog Circuit Fault Diagnosis Approach Based on Improved Wavelet Transform and MKELM
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2. Research on Fault Diagnosis of Drilling Pump Fluid End Based on Time-Frequency Analysis and Convolutional Neural Network;Processes;2024-09-08
3. Fault diagnosis of rolling bearings based on dynamic convolution and dual-channel feature fusion under variable working conditions;Measurement Science and Technology;2024-03-08
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