Enhancing Rolling Bearing Fault Diagnosis in Motors Using the OCSSA-VMD-CNN-BiLSTM Model: A Novel Approach for Fast and Accurate Identification
Author:
Affiliation:
1. College of Electronic and Electrical Engineering, Lanzhou Petrochemical University of Vocational Technology, Lanzhou, China
2. School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu, China
Funder
National Natural Science Foundation of China
Gansu Youth Science and Technology Fund
Natural Science Foundation of Gansu Province
Innovative Ability Enhancement Project of Gansu Provincial Higher Education
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx8/6287639/10380310/10546253.pdf?arnumber=10546253
Reference47 articles.
1. Bearing Fault Diagnosis With Envelope Analysis and Machine Learning Approaches Using CWRU Dataset
2. Adaptive Suppression of Mode Mixing in CEEMD Based on Genetic Algorithm for Motor Bearing Fault Diagnosis
3. Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network
4. An Intelligent Fault Diagnosis Method of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network
5. Discrete wavelet transform based processing of embroidered textile-electrode electromyography signal acquired with load and pressure effect
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1. Research on Fault Diagnosis of Drilling Pump Fluid End Based on Time-Frequency Analysis and Convolutional Neural Network;Processes;2024-09-08
2. Wavelet-Enhanced Time-Frequency Analysis Method for Bearing Fault Detection of Rotating Machinery;IEEE Access;2024
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