基于动态EM-SHSMM的异常数据下设备健康预测研究
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摘要
针对设备退化过程中异常数据下的剩余有效寿命预测问题,提出了一种基于动态的期望最大化算法(EM)-分段隐半马尔可夫模型(SHSMM)预测方法。基于SHSMM的理论框架,采用期望最大化参数自适应估计算法估计模型中的未知参数;基于WGM(1,1)模型,提出动态前向后向灰色填充算法处理样本中的异常数据,并利用健康预测过程预测设备的剩余有效寿命;通过实例分析对模型进行评价和验证。结果表明,提出的设备健康预测方法能有效解决异常数据的问题。
Aiming at the problem of remaining useful life prognosis under abnormal data during equipment degradation,this paper developed a prognostic method based on dynamic expectation maximization(EM)-segmented hidden semi-Markov model(SHSMM). Firstly,based on the SHSMM model framework,it used the expectation maximization algorithm to estimate the unknown parameters of the model. Secondly,to process the anomaly data in the samples,it proposed a dynamic forward-backward gray-fill algorithm based on WGM(1,1),and it carried out the equipment health prognosis. Finally,it used a case study to evaluate the performance of the model. The results show that the proposed method could effectively solve the problem of abnormal data.
Aiming at the problem of remaining useful life prognosis under abnormal data during equipment degradation,this paper developed a prognostic method based on dynamic expectation maximization(EM)-segmented hidden semi-Markov model(SHSMM). Firstly,based on the SHSMM model framework,it used the expectation maximization algorithm to estimate the unknown parameters of the model. Secondly,to process the anomaly data in the samples,it proposed a dynamic forward-backward gray-fill algorithm based on WGM(1,1),and it carried out the equipment health prognosis. Finally,it used a case study to evaluate the performance of the model. The results show that the proposed method could effectively solve the problem of abnormal data.
引文
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[14]陈雄姿,于劲松,唐荻音,等.基于贝叶斯LS-SVR的锂电池剩余寿命概率性预测[J].航空学报,2013,34(9):2219-2229.(Chen Xiongzi,Yu Jinsong,Tang Diyin,et al. Probabilistic residual life prediction for lithium-ion Batteries based on Bayesian LS-SVR[J]. Acta Aeronautica et Astronautica Sinica,2013,34(9):2219-2229.
[15]曾绍华,魏延,唐远炎.剔除支持向量回归中异常数据算法[J].重庆大学学报:自然科学版,2012,35(12):120-132.(Zeng Shaohua,Wei Yan,Tang Yuanyan. Algorithm of removing outliers in SVR[J]. Journal of Chongqing University:Natural Science Edition,2012,35(12):120-132.)
[16]徐艺文,徐宁彬,庄重文,等.面向群智感知车联网的异常数据检测算法[J].湖南大学学报:自然科学版,2017,44(8):145-151.(Xu Yiwen,Xu Ningbin,Zhuang Zhongwen,et al. An algorithm of abnormal data detection for internet of vehicles based on crowdsensing[J]. Journal of Hunan University:Naturnal Science,2017,44(8):145-151.)
[17]Si Xiaosheng,Hu Changhua,Zhou Donghua. Nonlinear degradation process modeling and remaining useful life estimation subject to measurement error[J]. Acta Automatica Sinica,2013,39(5):530-541.
[2] Peng C Y,Tseng S T. Mis-specification analysis of linear degradation models[J]. IEEE Trans on Reliability,2009,58(3):444-455.
[3] Carr M J,Wangbc W. An approximate algorithm for prognostic modelling using condition monitoring information[J]. European Journal of Operational Research,2011,211(1):90-96.
[4] Kharoufeh J P,Cox S M,Oxley M E. Reliability of manufacturing equipment in complex environments[J]. Annals of Operations Research,2013,209(1):231-254.
[5] Dong M,He D,Banerjee P,et al. Equipment health diagnosis and prognosis using hidden semi-Markov models[J]. International Journal of Advanced Manufacturing Technology,2006,30(7):738-749.
[6] Dong M,He D. Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis[J]. European Journal of Operational Research,2007,178(3):858-878.
[7] Peng Y,Dong M. A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction[J]. Mechanical Systems&Signal Processing,2011,25(1):237-252.
[8]刘勤明,李亚琴,吕文元,等.基于自适应隐式半马尔可夫模型的设备健康诊断与寿命预测方法[J].计算机集成制造系统,2016,22(9):2187-2194.(Liu Qinming,Li Yaqin,Lyu Wen yuan,et al.Equipment health diagnostics and prognostics method based on adaptive HSMM[J]. Computer Integrated Manufacturing Systems,2016,22(9):2187-2194.)
[9] Fang Xiaolei,Zhou Rensheng,Gebraeel N. An adaptive functional regression-based prognostic model for applications with missing data[J]. Reliability Engineering&System Safety,2015,133(1):266-274.
[10]武昭融,李秀君,李梦晨,等.基于衰变-Markov模型的沥青路面性能预测研究[J].上海理工大学学报,2016,38(2):187-191.(Wu Zhaorong,Li Xiujun,Li Mengchen,et al. Pavement performance prediction of highway based on the decay-Markov prediction model[J].Journal of University of Shanghai for Science and Technology,2016,38(2):187-191.)
[11]Daroogheh N,Baniamerian A,Meskin N,et al. Prognosis and health monitoring of nonlinear systems using a hybrid scheme through integration of PFs and neural networks[J]. IEEE Trans on Systems Man&Cybernetics Systems,2017,47(8):1990-2004.
[12]Zhao Fuqiong,Xie Mingjiang,Tian Zhigang,et al. Integrated equipment health prognosis considering crack initiation time uncertainty and random shock[J]. Chinese Journal of Mechanical Engineering,2017,30(6):1383-1395.
[13]冯磊,王宏力,司小胜,等.基于半随机滤波—期望最大化算法的剩余寿命在线预测[J].航空学报,2015,36(2):555-563.(Feng Lei,Wang Hongli,Si Xiaosheng,et al. Real-time residual life prediction based on semi-stochastic filter and expectation maximization algorithm[J]. Acta Aeronautica et Astronautica Sinica,2015,36(2):555-563.)
[14]陈雄姿,于劲松,唐荻音,等.基于贝叶斯LS-SVR的锂电池剩余寿命概率性预测[J].航空学报,2013,34(9):2219-2229.(Chen Xiongzi,Yu Jinsong,Tang Diyin,et al. Probabilistic residual life prediction for lithium-ion Batteries based on Bayesian LS-SVR[J]. Acta Aeronautica et Astronautica Sinica,2013,34(9):2219-2229.
[15]曾绍华,魏延,唐远炎.剔除支持向量回归中异常数据算法[J].重庆大学学报:自然科学版,2012,35(12):120-132.(Zeng Shaohua,Wei Yan,Tang Yuanyan. Algorithm of removing outliers in SVR[J]. Journal of Chongqing University:Natural Science Edition,2012,35(12):120-132.)
[16]徐艺文,徐宁彬,庄重文,等.面向群智感知车联网的异常数据检测算法[J].湖南大学学报:自然科学版,2017,44(8):145-151.(Xu Yiwen,Xu Ningbin,Zhuang Zhongwen,et al. An algorithm of abnormal data detection for internet of vehicles based on crowdsensing[J]. Journal of Hunan University:Naturnal Science,2017,44(8):145-151.)
[17]Si Xiaosheng,Hu Changhua,Zhou Donghua. Nonlinear degradation process modeling and remaining useful life estimation subject to measurement error[J]. Acta Automatica Sinica,2013,39(5):530-541.