Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: A method for bearing prognostics
Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: A method for bearing prognostics
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DOI：
10.1016/j.jsv.2007.01.001
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发表时间：
2007-05-22
影响因子：
4.7
通讯作者：
Discenzo, Fred M.
中科院分区：
文献类型：
--
作者：
Ocak, Hasan;Loparo, Kenneth A.;Discenzo, Fred M.
轴承是旋转机械中常见的重要部件。通过跟踪轴承的状态，可以避免由轴承故障引起的计划外机械停机和昂贵的损坏。本文提出了一种基于小波包分解和隐马尔可夫建模（HMM）的轴承故障严重程度跟踪方案。该方案将振动信号分解成小波包，并以分解树的节点能量作为特征。基于从轴承正常振动信号中提取的特征，训练HMM对正常跳动工况进行建模。然后使用该HMM的概率来跟踪轴承的状态。从轴承加速寿命试验中收集的实验数据表明，与许多其他常用趋势参数不同，随着轴承损伤的增加，其特征会降低到正常的类似轴承的水平，正常轴承HMM的概率随着轴承损伤向轴承失效的进展而不断降低。当轴承接近其寿命（剩余寿命的10%）时，HMM概率急剧下降，表明轴承严重损坏和即将失效。(c) 2007 Elsevier Ltd.版权所有。
Bearings are common and vital elements in rotating machinery. By tracking the condition of a bearing, unscheduled machinery outages and costly damage caused by a bearing failure can be avoided. In this paper, we developed a new scheme based on wavelet packet decomposition and hidden Markov modeling (HMM) for tracking the severity of bearing faults. In this scheme, vibration signals were decomposed into wavelet packets and the node energies of the decomposition tree were used as features. Based on the features extracted from normal bearing vibration signals, an HMM was trained to model the normal beating operating condition. The probabilities of this HMM were then used to track the condition of the bearing. Experimental data collected from a bearing accelerated life test showed that unlike many of the other commonly used trend parameters whose distinguishing features diminished to normal bearing-like levels as the damage grew, the probabilities of the normal bearing HMM kept decreasing as the bearing damage progressed toward bearing failure. As the bearing approached the end of its life (10% of remaining life), the HMM probabilities dropped dramatically signaling severe damage and imminent bearing failure. (c) 2007 Elsevier Ltd. All rights reserved.