One-Dimensional LSTM-Regulated Deep Residual Network for Data-Driven Fault Detection in Electric Machines
Author:
Affiliation:
1. Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/41/10255541/10100645.pdf?arnumber=10100645
Reference38 articles.
1. Deep Residual Networks With Adaptively Parametric Rectifier Linear Units for Fault Diagnosis
2. A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing
3. Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review
4. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders
5. RegNet: Self-Regulated Network for Image Classification
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