A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis-Reference-Cited by-同舟云学术

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A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

Published:2017-12-11 Issue:12 Volume:17 Page:2876
ISSN:1424-8220
Container-title:Sensors
language:en
Short-container-title:Sensors

Author:

Sohaib Muhammad,Kim Cheol-Hong,Kim Jong-Myon

Funder

Korea Institute of Energy Technology Evaluation and Planning

National Research Foundation of Korea

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Link

http://www.mdpi.com/1424-8220/17/12/2876/pdf

Reference47 articles.

1. Electric Machines: Modeling, Condition Monitoring and Fault Diagnosis;Toliyat,2012

2. Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement

3. Reliable bearing fault diagnosis using Bayesian inference-based multi-class support vector machines

4. Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions

5. A Novel Faults Diagnosis Method for Rolling Element Bearings Based on EWT and Ambiguity Correlation Classifiers

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