Fault Diagnosis Method for Imbalanced Data of Rotating Machinery Based on Time Domain Signal Prediction and SC-ResNeSt
Author:
Affiliation:
1. School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an, China
Funder
Key Research and Development Program of Shaanxi Province
Key Laboratory of Expressway Construction Machinery of Shaanxi Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10105303.pdf?arnumber=10105303
Reference54 articles.
1. Opportunities and Challenges of Machinery Intelligent Fault Diagnosis in Big Data Era
2. Deep Transfer Learning in Mechanical Intelligent Fault Diagnosis: Application and Challenge
3. A hybrid deep-learning model for fault diagnosis of rolling bearings
4. Information Fusion Fault Diagnosis Method for Deep-Sea Human Occupied Vehicle Thruster Based on Deep Belief Network
5. Fault Detection and Diagnosis in Electric Motors Using Convolution Neural Network and Short-Time Fourier Transform
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