小波包变换和隐马尔可夫模型在轴承性能退化评估中的应用

肖文斌;陈进;周宇;王志阳

振动与冲击 ›› 2011, Vol. 30 ›› Issue (8) : 32-35.

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振动与冲击 ›› 2011, Vol. 30 ›› Issue (8) : 32-35.
论文

小波包变换和隐马尔可夫模型在轴承性能退化评估中的应用

  • 肖文斌; 陈进; 周宇; 王志阳
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Wavelet Packet Transform and Hidden Markov Models based Bearing Performance Degradation Assessment

  • XIAO Wen-bin; CHEN Jin; ZHOU Yu; WANG Zhi-yang
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摘要

轴承是旋转机械中的关键部件,有效地对其进行性能退化评估对指导设备维护、防止设备意外失效有非常重要的意义。本文提出了一种基于小波包变换和隐马尔可夫模型(HMM)的轴承性能退化评估方法。该方法使用小波包变换对轴承振动信号进行分析,并提取节点能量及其总能量作为特征,仅使用正常状态下的数据训练HMM,建立性能退化评估模型,然后使用该模型对轴承的退化程度进行定量评估。最后,通过对轴承加速疲劳寿命试验的研究,验证了所提出的方法的可行性和有效性。

Abstract

Bearings are one of the key components in rotating machinery. Therefore, it is important to assess the performance degradation degree of bearings for making maintenance plans and preventing unexpected defects and breakdowns during operation. In this paper, we present a novel bearing performance degradation assessment methodology based on wavelet packet transform (WPT) and hidden Markov models (HMMs). WPT is used to extract features from vibration signals of bearings, and the node energies and the total energy are selected as features. An HMM is trained using the data under normal condition and then the trained HMM is used to assess the performance degradation degree of bearings quantitatively. A bearing accelerated life test is performed to validate the proposed methodology. The experimental results show that the proposed methodology is feasible and effective.

关键词

性能退化评估 / 小波包变换 / 隐马尔可夫模型

Key words

Performance degradation assessment / Wavelet packet transform / Hidden Markov model

引用本文

导出引用
肖文斌;陈进;周宇;王志阳. 小波包变换和隐马尔可夫模型在轴承性能退化评估中的应用[J]. 振动与冲击, 2011, 30(8): 32-35
XIAO Wen-bin;CHEN Jin;ZHOU Yu;WANG Zhi-yang. Wavelet Packet Transform and Hidden Markov Models based Bearing Performance Degradation Assessment[J]. Journal of Vibration and Shock, 2011, 30(8): 32-35

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