Remaining Useful Life Prediction for Circuit Breaker Based on SM-CFE and SA-BiLSTM
Author:
Affiliation:
1. School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
2. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China
Funder
Natural Science Foundation of Hebei Province
Foundation for Creative Research Groups of Hebei Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/10131956.pdf?arnumber=10131956
Reference36 articles.
1. A Deep Domain Adaptative Network for Remaining Useful Life Prediction of Machines Under Different Working Conditions and Fault Modes
2. Multiscale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery
3. MSWR-LRCN: A new deep learning approach to remaining useful life estimation of bearings
4. DCNN based human activity recognition framework with depth vision guiding
5. Chaotic Analysis and Feature Extraction of Vibration Signals From Power Circuit Breakers
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