National Changhua University of Education Institutional Repository : Item 987654321/18360

English | 正體中文 | 简体中文 | Items with full text/Total items : 6507/11669
Visitors : 30045167 Online Users : 570

RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope Adv. Search

Login ‧ Upload ‧ Help ‧ About ‧ Administer

NCUEIR > College of Technology > vr > Periodical Articles > Item 987654321/18360

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/18360

Title:	Fault Gear Identification and Classification Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference
Authors:	Wu, Jian-Da;Hsu, Chuang-Chin;Wu, Guo-Zhen
Contributors:	車輛科技研究所
Keywords:	Fault diagnosis;Vibration signal;Discrete wavelet transform;Adaptive neuro-fuzzy interference;Energy spectrum
Date:	2009-04
Issue Date:	2014-04-29T07:28:11Z
Publisher:	Elsevier Ltd
Abstract:	In this paper, an intelligent diagnosis for fault gear identification and classification based on vibration signal using discrete wavelet transform and adaptive neuro-fuzzy inference system (ANFIS) is presented. The discrete wavelet transform (DWT) technique plays one of the important roles for signal feature extraction in the proposed system. The abnormal transient signals will show in different decomposition levels and can be used to recognize the various faults by the DWT figure. However, many fault conditions are hard to inspect accurately by the naked eye. In the present study, the feature extraction method based on discrete wavelet transform with energy spectrum is proposed. The different order wavelets are considered to identify fault features accurately. The database is established by feature vectors of energy spectrum which are used as input pattern in the training and identification process. Furthermore, the ANFIS is proposed to identify and classify the fault gear positions and the gear fault conditions in the fault diagnosis system. The proposed ANFIS includes both the fuzzy logic qualitative approximation and the adaptive neural network capability. The experimental results verified that the proposed ANFIS has more possibilities in fault gear identification. The ANFIS achieved an accuracy identification rate which was more satisfactory than traditional vision inspection in the proposed system.
Relation:	Expert Systems with Applications, 36(3)Part2: 6244-6255
Appears in Collections:	[vr] Periodical Articles

Files in This Item:

File	Size	Format
index.html	0Kb	HTML	592	View/Open

Loading...