Comparison of four direct classification methods for intelligent fault diagnosis of rotating machinery-Reference-Cited by-同舟云学术

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Comparison of four direct classification methods for intelligent fault diagnosis of rotating machinery

Published:2016-09 Issue: Volume:46 Page:459-468
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Dou Dongyang,Zhou Shishuai

Funder

Natural Science Foundation of Jiangsu Province

Jiangsu Key Laboratory of Large Engineering Equipment Detection and Control

Science & Technology Pillar Program of Jiangsu Province

Priority Academic Program Development of Jiangsu Higher Education Institutions

Publisher

Elsevier BV

Subject

Software

Reference37 articles.

1. Fault diagnosis of ball bearings using machine learning methods;Kankar;Expert Syst. Appl.,2011

2. Vibration analysis of rotating machinery using time–frequency analysis and wavelet techniques;Al-Badour;Mech. Syst. Signal Process.,2011

3. Ant colony clustering analysis based intelligent fault diagnosis method and its application to rotating machinery;Zhao;Pattern Anal. Appl.,2013

4. Fault diagnosis of ball bearings using continuous wavelet transform;Kankar;Appl. Soft Comput.,2011

5. A rule-based intelligent method for fault diagnosis of rotating machinery;Dou;Knowl. Based Syst.,2012

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