Discrete wavelet transform and artificial neural network for gearbox fault detection based on acoustic signals

被引：0
|
作者：
Abad, Mohammad Reza Asadi Asad [1 ]
Ahmadi, Hojat [2 ]
Moosavian, Ashkan [3 ]
Khazaee, Meghdad [3 ]
Kohan, Mohammad Ranjbar [1 ]
Mohammadi, Masoud [1 ]
机构：
[1] Islamic Azad Univ, Buinzahra Branch, Dept Mech Engn, Buinzahra, Iran
[2] Univ Tehran, Dept Mech Engn Agr Machinery, Karaj, Iran
[3] Tarbiat Modares Univ, Dept Mech Engn Agr Machinery, Tehran, Iran
来源：
JOURNAL OF VIBROENGINEERING | 2013年 / 15卷 / 01期
关键词：
gearbox; wavelet transform; artificial neural network; acoustic signal; MODEL; DIAGNOSIS;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Gearboxes are widely applied in power transmission lines, so their health monitoring has a great impact in industrial applications. In the present study, acoustic signals of Pride gearbox in different conditions, namely, healthy, worn first gear and broken second gear are collected by a microphone. Discrete wavelet transform (DWT) is applied to process the signals. Decomposition is made using Daubichies-5 wavelet with five levels. In order to identify the various conditions of the gearbox, artificial neural network (ANN) is used in decision-making stage. The results indicate that this method allow identification at a 90 % level of efficiency. Therefore, the proposed approach can be reliably applied to gearbox fault detection.
引用
收藏
页码：459 / 463
页数：5
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