滚动轴承寿命动态预测新方法
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摘要
由于传统可靠性分析方法是基于大量的失效试验数据和经验知识建立的静态模型,无法实现滚动轴承退化过程的跟踪以及准确进行可靠性评估和寿命预测,提出了基于主成分分析(principal component analysis,简称PCA)和相空间重构的滚动轴承寿命动态预测方法。首先,通过PCA将实时监测的多个滚动轴承性能指标进行融合;其次,使用相空间重构技术实现当前退化过程和历史退化过程的对比,得到寿命预测值,并结合历史失效时间进行统计推断,得到更准确的平均寿命。随着观测样本的不断积累,可实现平均寿命的动态更新。试验结果表明,本研究提出的动态寿命预测模型能够实时预测滚动轴承的寿命,具有较强的工程实用价值。
Research of the reliability evaluation and life prediction technology is of great significance to preventing faults and supporting predictive maintenance during rolling bearing running.The traditional reliability analysis methods with a static model based on large numbers of failure date and empirical knowledge are unable to track the degradation process of rolling bearing and accurate reliability assessment and life prediction.In this thesis,the dynamic reliability analysis model is established based on the principal component analysis(PCA)and phase space reconstruction.The real-time monitoring parameters are fused through PCA,and then the predictive value of life is obtained by comparing the current degradation process with the historical degradation process.With the continuous accumulation of observed samples,the life of rolling bearing can be updated.The experimental result shows that the dynamic life prediction model proposed in this paper can predict the life of the rolling bearing in real time.
Research of the reliability evaluation and life prediction technology is of great significance to preventing faults and supporting predictive maintenance during rolling bearing running.The traditional reliability analysis methods with a static model based on large numbers of failure date and empirical knowledge are unable to track the degradation process of rolling bearing and accurate reliability assessment and life prediction.In this thesis,the dynamic reliability analysis model is established based on the principal component analysis(PCA)and phase space reconstruction.The real-time monitoring parameters are fused through PCA,and then the predictive value of life is obtained by comparing the current degradation process with the historical degradation process.With the continuous accumulation of observed samples,the life of rolling bearing can be updated.The experimental result shows that the dynamic life prediction model proposed in this paper can predict the life of the rolling bearing in real time.
引文
[1]李海波.智能化轴承故障诊断仪的工程设计与研制[D].沈阳:沈阳理工大学,2009.
[2]苗学问,航空发动机主轴承使用寿命预测技术研究[D].北京:北京航空航天大学,2008.
[3]Gebraeel N,Lawley M.A neural network degradation model for computing and updating residual life distributions[J].IEEE Transactions on Automation Science and Engineering,2008,5(1):154-163.
[4]Sun Chuang,Zhang Zhousuo,He Zhengjia.Research on bearing life prediction based on support vector machine and its application[C]∥9th International Conference on Damage Assessment of Structures(DAMAS 2011).Oxford,UK:[s.n.],2011:12-28.
[5]申中杰,陈雪峰,何正嘉,等.基于相对特征和多变量支持向量机的滚动轴承剩余寿命预测[J].机械工程学报,2013,49(2):183-189.Shen Zhongjie,Chen Xuefeng,He Zhengjia,et al.Remaining life predictions of rolling bearing based on relative features and multivariable support vector machine[J].Journal of Mechanical Engineering,2013,49(2):183-189.(in Chinese)
[6]Ali J,Chebel-Morello B,Saidi L,et al.Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network[J].Mechanical Systems&Signal Processing,2015(S56/57):150-172.
[7]Li Naipeng,Lei Yaguo,Lin Jing,et al.An improved exponential model for predicting remaining useful life of rolling element bearings[J].IEEE Transactions on Industrial Electronics,2015,62(12):7762-7773.
[8]王奉涛,陈旭涛,柳晨曦,等.基于KPCA和WPHM的滚动轴承可靠性评估与寿命预测[J].振动、测试与诊断,2017,37(3):476-483.Wang Fengtao,Chen Xutao,Liu Chenxi,et al.Reliability evaluation and life prediction of rolling bearing based on KPCA and WPHM[J].Journal of Vibration,Measurement&Diagnosis,2017,37(3):476-483.(in Chinese)
[9]李常有,徐敏强,郭耸.声信号的主分量分析应用于滚动轴承故障诊断[J].声学技术,2008,27(2):271-275.Li Changyou,Xu Minqiang,Guo Song.Fault diagnosis of rolling element bearing based on principal component analysis of acoustic signal[J].Technical Acoustics,2008,27(2):271-275.(in Chinese)
[10]马红光,李夕海,王国华,等.相空间重构中嵌入维和时间延迟的选择[J].西安交通大学学报,2004,38(4):335-339.Ma Hongguang,Li Xihai,Wang Guohua,et al.Selection of embedding dimension and delay time in phase space reconstruction[J].Journal of Xi'an Jiaotong University,2004,38(4):335-339.(in Chinese)
[11]Qiu Hai,Lee J,Lin Jing,et al.Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J].Journal of Sound&Vibration,2006,289(4):1066-1090.
[12]Williams T,Ribadeneira X,Billington S,et al.Rolling element bearing diagnostics in run-to-failure lifetime testing[J].Mechanical Systems and Signal Processing2001,15(5):979-993.
[13]Wang Tianyi.Bearing life prediction based on vibration signals:a case study and lessons learned[C]∥Prognostics and Health Management.[S.l.]:IEEE,2012:1-7.
[14]Sutrisno E,Oh H,Vasan A S S,et al.Estimation of remaining useful life of ball bearings using data driven methodologies[C]∥Prognostics and Health Management.[S.l.]:IEEE,2012:1-7.
[2]苗学问,航空发动机主轴承使用寿命预测技术研究[D].北京:北京航空航天大学,2008.
[3]Gebraeel N,Lawley M.A neural network degradation model for computing and updating residual life distributions[J].IEEE Transactions on Automation Science and Engineering,2008,5(1):154-163.
[4]Sun Chuang,Zhang Zhousuo,He Zhengjia.Research on bearing life prediction based on support vector machine and its application[C]∥9th International Conference on Damage Assessment of Structures(DAMAS 2011).Oxford,UK:[s.n.],2011:12-28.
[5]申中杰,陈雪峰,何正嘉,等.基于相对特征和多变量支持向量机的滚动轴承剩余寿命预测[J].机械工程学报,2013,49(2):183-189.Shen Zhongjie,Chen Xuefeng,He Zhengjia,et al.Remaining life predictions of rolling bearing based on relative features and multivariable support vector machine[J].Journal of Mechanical Engineering,2013,49(2):183-189.(in Chinese)
[6]Ali J,Chebel-Morello B,Saidi L,et al.Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network[J].Mechanical Systems&Signal Processing,2015(S56/57):150-172.
[7]Li Naipeng,Lei Yaguo,Lin Jing,et al.An improved exponential model for predicting remaining useful life of rolling element bearings[J].IEEE Transactions on Industrial Electronics,2015,62(12):7762-7773.
[8]王奉涛,陈旭涛,柳晨曦,等.基于KPCA和WPHM的滚动轴承可靠性评估与寿命预测[J].振动、测试与诊断,2017,37(3):476-483.Wang Fengtao,Chen Xutao,Liu Chenxi,et al.Reliability evaluation and life prediction of rolling bearing based on KPCA and WPHM[J].Journal of Vibration,Measurement&Diagnosis,2017,37(3):476-483.(in Chinese)
[9]李常有,徐敏强,郭耸.声信号的主分量分析应用于滚动轴承故障诊断[J].声学技术,2008,27(2):271-275.Li Changyou,Xu Minqiang,Guo Song.Fault diagnosis of rolling element bearing based on principal component analysis of acoustic signal[J].Technical Acoustics,2008,27(2):271-275.(in Chinese)
[10]马红光,李夕海,王国华,等.相空间重构中嵌入维和时间延迟的选择[J].西安交通大学学报,2004,38(4):335-339.Ma Hongguang,Li Xihai,Wang Guohua,et al.Selection of embedding dimension and delay time in phase space reconstruction[J].Journal of Xi'an Jiaotong University,2004,38(4):335-339.(in Chinese)
[11]Qiu Hai,Lee J,Lin Jing,et al.Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J].Journal of Sound&Vibration,2006,289(4):1066-1090.
[12]Williams T,Ribadeneira X,Billington S,et al.Rolling element bearing diagnostics in run-to-failure lifetime testing[J].Mechanical Systems and Signal Processing2001,15(5):979-993.
[13]Wang Tianyi.Bearing life prediction based on vibration signals:a case study and lessons learned[C]∥Prognostics and Health Management.[S.l.]:IEEE,2012:1-7.
[14]Sutrisno E,Oh H,Vasan A S S,et al.Estimation of remaining useful life of ball bearings using data driven methodologies[C]∥Prognostics and Health Management.[S.l.]:IEEE,2012:1-7.
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