The Multiclass Fault Diagnosis of Wind Turbine Bearing Based on Multisource Signal Fusion and Deep Learning Generative Model
Author:
Affiliation:
1. Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin, China
Funder
International Science and Technology Cooperation Project of Jilin Province Science and Technology Department
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/9717300/09783155.pdf?arnumber=9783155
Reference34 articles.
1. Fault Diagnosis of Rolling Bearings Based on an Improved Stack Autoencoder and Support Vector Machine
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5. Implementation of Fault Diagnosis of Wind Turbine Based on Signal Analysis with NN Algorithm
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