A Fast and Adaptive Empirical Mode Decomposition Method and Its Application in Rolling Bearing Fault Diagnosis
Author:
Affiliation:
1. State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China
2. AECC Key Laboratory of Aero-Engine Vibration Technology, AECC Hunan Aviation Powerplant Research Institute, Zhuzhou, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/10003029/09966523.pdf?arnumber=9966523
Reference25 articles.
1. Open-Set Domain Adaptation in Machinery Fault Diagnostics Using Instance-Level Weighted Adversarial Learning
2. Universal Domain Adaptation in Fault Diagnostics With Hybrid Weighted Deep Adversarial Learning
3. Fault diagnosis of rotating machines based on the EMD manifold
4. A New Health Indicator Construction Approach and Its Application in Remaining Useful Life Prediction of Bearings
5. Research on the Recognition of Machining Conditions Based on Sound and Vibration Signals of a CNC Milling Machine
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