Rolling Bearing Remaining Useful Life Prognosis Method based on Improved CHSMM

Zhu Shuo; Bai Ruilin; Ji Feng

doi:10.16578/j.issn.1004.2539.2018.10.009

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Rolling Bearing Remaining Useful Life Prognosis Method based on Improved CHSMM

更新时间：2022-10-20

- Rolling Bearing Remaining Useful Life Prognosis Method based on Improved CHSMM
- Vol. 42, Issue 10, Pages: 46-52(2018)
- 作者机构：
  
  1. 江南大学轻工过程先进控制教育部重点实验室
  2. 无锡信捷电气股份有限公司
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2018.10.009
  CLC：
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[1]朱朔,白瑞林,吉峰.改进CHSMM的滚动轴承剩余寿命预测方法[J].机械传动,2018,42(10):46-52+95.

Zhu Shuo, Bai Ruilin, Ji Feng. Rolling Bearing Remaining Useful Life Prognosis Method based on Improved CHSMM. [J]. 42(10):46-52(2018)
[1]朱朔,白瑞林,吉峰.改进CHSMM的滚动轴承剩余寿命预测方法[J].机械传动,2018,42(10):46-52+95. DOI： 10.16578/j.issn.1004.2539.2018.10.009.

Zhu Shuo, Bai Ruilin, Ji Feng. Rolling Bearing Remaining Useful Life Prognosis Method based on Improved CHSMM. [J]. 42(10):46-52(2018) DOI： 10.16578/j.issn.1004.2539.2018.10.009.

摘要

针对连续隐半马尔科夫模型（Continuous hidden semi-Markov model,CHSMM）对滚动轴承剩余寿命预测精度低问题,提出一种基于改进CHSMM的滚动轴承剩余寿命方法。提取滚动轴承振动信号的时域、时频域特征向量,采用主成分分析（Principle component analysis,PCA）算法对特征向量进行降维;针对状态驻留时间概率密度函数不符合实际而引起的剩余寿命预测精度低问题,将高斯混合概率密度函数引入到CHSMM算法中,建立退化状态识别模型和剩余寿命预测模型。最后,将轴承全生命周期数据输入到模型中,得到轴承的退化状态和剩余寿命。试验结果表明,采用所提方法能准确的对轴承剩余寿命进行预测,与CHSMM算法相比,退化状态识别的正确率提高了12%,剩余寿命预测的正确率提高了23%。

Abstract

A rolling bearing remaining useful life prognostics method based on improved Continuous hidden semi-Markov model（ CHSMM） is proposed,aiming at the problem that the CHSMM algorithm prognostics accuracy is low for remaining useful life of rolling bearings. The feature vectors of the time and time frequency domain are extracted from the vibration signal of bearing and then the PCA algorithm is used to reduce the dimension of the feature vectors. Then,the degradation state recognition model and the remaining useful life prediction model are established based on improved CHSMM into which the Gauss mixture probability density function is introduced aiming at solving the low accuracy of remaining useful life prediction caused by the dwell time probability density function which does not conform to reality. Finally,the whole life cycle data of the bearing is input into the model,and the degenerate state and residual life of the bearing are obtained. The experimental results show that the proposed method can accurately predict the remaining useful life of bearings.Compared with the original CHSMM algorithm,the accuracy of the degradation state recognition is increased by12%,and the accuracy of remaining useful life prediction is increased by 23%.

关键词

轴承CHSMMPCA剩余寿命

Keywords

BearingCHSMMPCARemaining useful life

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