Corrosion Fault Diagnosis of Rolling Element Bearing under Constant and Variable Load and Speed Conditions

被引：13

作者：

Sharma, S. ^{[1
]}

Abed, W. ^{[1
]}

Sutton, R. ^{[1
]}

Subudhi, B. ^{[2
]}

机构：

[1] Univ Plymouth, Sch Marine Sci & Engn, Plymouth PL4 8AA, Devon, England

[2] Natl Inst Technol Rourkela, Dept Elect Engn, Odisha 769008, India

来源：

IFAC PAPERSONLINE | 2015年 / 48卷 / 30期

关键词：

Fault analysis; features extraction; dimensionality reduction; dynamic recurrent neural network; DISCRETE WAVELET TRANSFORM; INDUCTION MACHINES; NEURAL-NETWORKS; VIBRATION;

D O I：

10.1016/j.ifacol.2015.12.352

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Rolling element bearing defects is one of the main reasons for breakdown in electrical machines. Robust fault analysis (FA) including the diagnosis of faults and predicting their level of fault severity is thus necessary to optimise maintenance, improve reliability and to avoid more catastrophic failure consequences. The proposed diagnostic methods in this paper use the innovative discrete wavelet transform (DWT) for feature extraction and an orthogonal fuzzy neighbourhood discriminative analysis (OFNDA) approach for feature reduction. The dynamic recurrent neural network predicts the conditions of components and classifies faults under different operating conditions.. The results obtained from the real time simulation demonstrate the effectiveness and reliability of the proposed methodology in classifying the different faults faster and accurately. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：49 / 54

页数：6

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