CHMM和AR模型在轴承性能退化评估与预测中的应用

doi:10.16180/j.cnki.issn1007-7820.2020.05.010

Abstract

Abstract:

The classic fault diagnosis technology can evaluate the operating status of the system in real time, but in practical applications, it is more desirable to predict the occurrence of the fault to guarantee personal and economic security. As a key component of mechanical equipment, the damage of the bearing may cause serious engineering accidents, so the bearing needs to be diagnosed. In this study, a continuous hidden Markov model was introduced, and the log likelihood was used as an evaluation index to evaluate performance degradation. The logarithm likelihood ratio based on the model output was combined with the autoregressive model, and then was used to predict the performance degradation of the bearing. The validity of the method was verified by comparing the two sets of full-life data. The results showed that the bearing performance degradation evaluation method based on continuous hidden Markov model was effective in evaluating the degradation of bearing performance, and the autoregressive model obtained more accurate results in life prediction.

Key words: continuous hidden Markov model, autoregressive, trend extrapolation, performance degradation assessment, performance degradation prediction, feature extraction

CLC Number:

TP206 +.3

LIU Yimin,LIU Tao,CHEN Qing. Application of CHMM and AR Model in Evaluation and Prediction of Bearing Performance Degradation[J].Electronic Science and Technology, 2020, 33(5): 58-65.

Figures/Tables 19

Table1

Characteristic parameters"

序号	名称	定义
1	有效值	X_rms= $1 n ∑ i = 1 N x i 2$
2	峰峰值	X_pk=X_max-X_min
3	峭度	X_ku= $1 N ∑ i = 1 N x i 2$
4	歪度因子	X_ske= $1 N ∑ i = 1 N x i - μ σ 2$
5	波形因子	X_waf= $1 N ∑ i = 1 N x i 2 1 N ∑ i = 1 N \| x i \|$
6	峰值因子	X_pef= $max \| x i \| 1 N ∑ i = 1 N x i 2$
8	脉冲因子	X_puf= $max \| x i \| 1 N ∑ i = 1 N \| x i \|$
9	裕度因子	X_maf= $max \| x i \| 1 N ∑ i = 1 N \| X i \| 2$

Table1

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Application of CHMM and AR Model in Evaluation and Prediction of Bearing Performance Degradation

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