Fault Diagnosis of Switched Reluctance Motor Based on Deep Feature Fusion of Multisensor Signals
Author:
Affiliation:
1. Anhui University,College of Electrical Engineering and Automation,Hefei,China,230601
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10587298/10587321/10587793.pdf?arnumber=10587793
Reference13 articles.
1. Bearing Fault Diagnosis of Switched Reluctance Motor in Electric Vehicle Powertrain via Multisensor Data Fusion
2. A fuzzy system of operation safety assessment using multi-model linkage and multi-stage collaboration for in-wheel motor
3. Matching synchrosqueezing transform: A useful tool for characterizing signals with fast varying instantaneous frequency and application to machine fault diagnosis
4. A novel feature enhancement framework for rotating machinery fault identification under limited datasets
5. Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals
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