期刊
APPLIED SOFT COMPUTING
卷 12, 期 9, 页码 2867-2878出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.asoc.2012.04.003
关键词
Damage diagnosis; Wavelet transform; Neural networks; Dynamic monitoring
资金
- Ministry of Science and Innovation of Spain [BIA2010-20234-C03-01]
This paper presents a multi-stage algorithm for the dynamic condition monitoring of a gear. The algorithm provides information referred to the gear status (fault or normal condition) and estimates the mesh stiffness per shaft revolution in case that any abnormality is detected. In the first stage, the analysis of coefficients generated through discrete wavelet transformation (DWT) is proposed as a fault detection and localization tool. The second stage consists in establishing the mesh stiffness reduction associated with local failures by applying a supervised learning mode and coupled with analytical models. To do this, a multi-layer perceptron neural network has been configured using as input features statistical parameters sensitive to torsional stiffness decrease and derived from wavelet transforms of the response signal. The proposed method is applied to the gear condition monitoring and results show that it can update the mesh dynamic properties of the gear on line. (C) 2012 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据