Explainable machine learning for motor fault diagnosis
Author:
Affiliation:
1. University of Kentucky,Department of Electrical and Computer Engineering,Lexington,USA
2. Department of Electrical and Computer Engineering & Department of Mechanical and Aerospace Engineering,Lexington,USA
Funder
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10175865/10175876/10175895.pdf?arnumber=10175895
Reference27 articles.
1. Neural-Network approaches for classification of induction machine faults using optimal time-frequency representations
2. Fault signal propagation in a network of distributed motors
3. Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
4. Vibration Signal Analysis for Electrical Fault Detection of Induction Machine Using Neural Networks;hua;Neural Computing and Applications,2011
5. Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique
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3. Enhancing Reliability Through Interpretability: A Comprehensive Survey of Interpretable Intelligent Fault Diagnosis in Rotating Machinery;IEEE Access;2024
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