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

摘要/Abstract

摘要：

经典的故障诊断技术可对系统运行状态进行实时评估,但在实际应用中更希望预知故障的发生以便对人身、经济安全做出保障。轴承作为机械设备的关键部件,其损坏会造成严重的工程事故,因此需对轴承进行故障诊断。文中引入连续隐马尔可夫模型以对数似然数作为评估指标来评估性能退化,即利用基于模型输出的对数似然率结合自回归模型对轴承进行性能退化预测。为了更好地验证方法有效性,文中使用了两组全寿命数据互为对比。结果显示,使用基于连续隐马尔可夫模型的轴承性能退化评估法对轴承性能的退化进行评估效果良好,自回归模型在寿命预测上得到了较为精准的结果。

关键词: 连续隐马尔可夫模型, 自回归, 趋势外推, 性能退化评估, 性能退化预测, 特征提取

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

中图分类号:

TP206 +.3

刘义民,刘韬,陈庆. CHMM和AR模型在轴承性能退化评估与预测中的应用[J]. 电子科技, 2020, 33(5): 58-65.

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.

图/表 19

表1

特征参数"

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= $max \| x i \| 1 N ∑ i = 1 N \| X i \| 2$

表1

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表3

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参考文献 17

[1]	Dragan Djurdjanovic, Jay Lee, Jun Ni . Watchdog Agent-an infotronics based prognostics approach for product performance degradation assessment and prediction[J]. Advanced Engineering Informatics, 2004,17(3):54-65.
[2]	陈进 . 机械设备振动监测与故障诊断[M]. 上海: 上海交通大学出版社, 1999.
	Chen Jin. Mechanical equipment vibration monitoring and fault diagnosis[M]. Shanghai: Shanghai Jiao Tong University Press, 1999.
[3]	钟秉林, 黄仁 . 机械故障诊断学[M]. 北京: 机械工业出版社, 2007.
	Zhong Binglin, Huang Ren. Mechanical fault diagnosis [M]. Beijing: China Mechanical Press, 2007.
[4]	Worden K, Staszewski W J, Hensman J J . Natural computing for mechanical systems research: A tutorial overview[J]. Mechanical Systems and Signal Processing, 2011,25(1):4-111.
[5]	王恒, 马海波, 徐海黎 , 等. 机械设备性能退化评估与预测研究综述[J]. 机械强度, 2013,35(6):716-723.
	Wang Heng, Ma Haibo, Xu Haili , et al. Review on machinery performance degradation assessment and prognostics[J]. Journal of Mechanical Strength, 2013,35(6):716-723.
[6]	张小丽, 陈雪峰, 李兵 , 等. 机械重大装备寿命预测综述[J]. 机械工程学报, 2011,47(11):100-116.
	Zhang Xiaoli, Chen Xuefeng, Li Bing , et al. Review of life prediction for mechanical major equipments[J]. Journal of Mechanical Engineering, 2011,47(11):100-116.
[7]	李奕江, 张金萍, 李允公 . 基于VMD-HMM的滚动轴承磨损状态识别[J]. 振动与冲击, 2018,37(21):61-67.
	Li Yijiang, Zhang Jinping, Li Yungong . Wear state recognition of rolling bearings based on VMD-HMM[J]. Journal of Vibration and Shock, 2018,37(21):61-67.
[8]	蔡金锭, 鄢仁武 . ARMA双谱分析与离散隐马尔可夫模型在电力电子电路故障诊断中的应用[J]. 中国电机工程学报, 2010,30(24):54-60.
	Cai Jinding, Yan Renwu . Fault diagnosis of power electronic circuit applying ARMA bispectrum and discrete hidden Markov model[J]. Proceedings of the CSEE, 2010,30(24):54-60.
[9]	从飞云, 陈进, 董广明 . 基于AR模型的Kolmogorov-Smirnov检验性能退化及预测研究[J]. 振动与冲击, 2012,31(10):79-82.
	Cong Feiyun, Chen Jin, Dong Guangming . Performance degradation assessment by Kolmogorov-Smirnov test and prognosis based on AR model[J]. Journal of Vibration and Shock, 2012,31(10):79-82.
[10]	Qinming Liu, Ming Dong, Ying Peng . A novel method for online health prognosis of equipment based on hidden semi-Markov model using sequential Monte Carlo methods[J]. Mechanical Systems and Signal Processing, 2012,32(8):341-348.
[11]	张雪峰 . 滚动轴承故障诊断与性能退化评估[D]. 哈尔滨:哈尔滨工业大学, 2016.
	Zhang Xuefeng . Faultdlagnosis and performance degradation assessment of rolling bearing[D]. Harbin:Harbin Institute of Technology, 2016.
[12]	滕红智, 赵建民, 贾希胜 , 等. 基于CHMM的齿轮箱状态识别研究[J]. 振动与冲击, 2012,31(5):92-96,127.
	Teng Hongzhi, Zhao Jianmin, Jia Xisheng , et al. Gearbox state recognition based on Continuous Hidden Markov Model[J]. Journal of Vibration and Shock, 2012,31(5):92-96,127.
[13]	Yu J . Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models[J]. Mech System Signal Processding, 2011,25(7):2573-2588.
[14]	张海兵 . 基于粒子滤波的动态自回归预测模型方法[J]. 电子科技, 2016,29(6):15-18.
	Zhang Haibing . Dynamic auto-regression prediction model based on particle filter[J]. Electronic Science and Technology, 2016,29(6):15-18
[15]	许国辉, 张新长 . 用AR模型建立变形预测模型的研究[J]. 中山大学学报(自然科学版), 2004(1):107-109.
	Xu Guohui, Zhang Xinchang . Research on establishing deformation prediction model with AR model[J].Journal of Sun Yat-sen University(Natural Science Edition), 2004(1):107-109.
[16]	季云, 王恒, 朱龙彪 , 等. 基于DPMM-CHMM的机械设备性能退化评估研究[J]. 振动与冲击, 2017,36(23):170-174.
	Ji Yun, Wang Heng, Zhu Longbiao , et al. Performance degradation assessment for mechanical equipment based on DPMM-CHMM[J]. Journal of Vibration and Shock, 2017,36(23):170-174.
[17]	杨德平, 刘喜华, 孙海涛 . 经济预测方法及MATLAB实现[M]. 北京: 机械工业出版社, 2012.
	Yang Deping, Liu Xihua, Sun Haitao. Economic forecasting method and MATLAB implementation[M]. Beijing: Mechanical Industry Press, 2012.