Reliability Prediction Method Based on State Space Model

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Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (1) : 118-124.

LI Hongkun1,2, HE Delu1,ZHANG Zhixin1, Ren Yuanjie1,2, Cong Ming1,2

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Abstract

Reliability analysis based on equipment's performance degradation characteristics is one of the important research directions for reliability technology. As many researchers work on multi-sample analysis, it is limit for single equipment reliability prediction. So, the method of reliability prediction based on state space model is proposed for single sample analysis. First, signals about machine working conditions are determined based on-line monitoring technology for equipment. Secondly, wavelet packet energy is used for characteristic extraction for the monitored signals. Frequency band energy is determined to be as characteristic parameter. Then, the degradation characteristics of signal to noise ratio is improved by moving average filtering processing. Finally, state space model was established to predict degradation characteristics of probability density distribution, and the degree of reliability is calculated. Two real testing example of bearing and milling cutter are used to demonstrate the rationality and effectiveness of this method. It’s a useful method for single sample reliability prediction.

Key words

Reliability prediction / state space model / feature extraction / wavelet analysis / moving average

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LI Hongkun1,2, HE Delu1, ZHANG Zhixin1, Ren Yuanjie1,2, Cong Ming1,2. Reliability Prediction Method Based on State Space Model[J]. Journal of Vibration and Shock, 2016, 35(1): 118-124

References

[1] 丁锋, 何正嘉, 訾艳阳等. 基于设备状态振动特征的比例故障率模型可靠性评估[J]. 机械工程学报，2009，45(12)：89-94.
     DING Feng, HE Zhengjia, ZI Yanyang, et al. Reliability assessment based on equipment condition vibration feature using proportional hazard model[J]. Journal of Mechanical Engineering, 2009, 45(12)：89-94.
[2]   陈保家，陈雪峰,李兵等. Logistic回归模型在机床刀具可靠性评估中的应用[J].机械工程学报, 2011,47(18)：158-164.
CHEN Baojia, CHEN Xuefeng, LI Bing, et al. Reliability estimation for cutting tool based on Logistic Regression Model[J]. Chinese Journal of Mechanical Engineering, 2011,47(18)：158-164..
[3] 何正嘉, 曹宏瑞, 訾艳阳等. 机械设备运行可靠性评估的发展与思考 [J/OL]. 机械工程学报. 2012. , http://www.cnki.net/kcms/detail/11.2187.TH.20130813.1355.022.html.
    HE Zhengjia CAO Hongrui ZI Yanyang, et al. Developments and Thoughts on Operational Reliability Assessment of Mechanical Equipment [J/OL]. Chinese Journal of Mechanical Engineering, http://www.cnki.net/kcms/detail/11.2187.TH.20130813.1355.022.html.
[4]   张星辉,康建设,赵劲松,等.基于混合高斯输出贝叶斯信念网络模型的设备退化状态识别与剩余使用寿命预测方法研究[J].振动与冲击,2014,33(8):171-179.
     ZHANG Xing-hui,KANG Jian-she,ZHAO Jin-song,et al.Equipment degradation state identification and residual life prediction based on MoG-BBN[J].Journal of Vibration and Shock,2014,33(8):171-179.
[5] ORCHARD M E, VACHTSEVANOS G J. A particle-filtering approach for on-line fault diagnosis and failure prognosis[J]. Transactions of the Institute of Measurement and Control, 2009, 31(3-4): 221-246.
[6] GASPERIN M, JURICIC D, BOSKOSKI P, et al. Model-based prognostics of gear health using stochastic dynamical models[J]. Mechanical Systems and Signal Processing, 2011, 25(2): 537-548.
[7] LU Huitian, WILLIAM J, SUSAN S. L. Real-time performance reliability prediction[J]. IEEE TRANSACTIONS ON RELIABILIT, 2001, 50(4): 353-357.
[8] XU Zhengguo, JI Yindong, ZHOU Donghua, et al. Real-time Reliability Prediction for a Dynamic System Based on the Hidden Degradation Process Identification[J]. IEEE TRANSACTIONS ON RELIABILITY, 2008, 57(2), 230-242.
[9] HUA Cheng, ZHANG Qing, XU Guanghua, et al. Performance reliability estimation method based on adaptive failure threshold[J]. Mechanical Systems and Signal Processing, 2012, http://dx.doi.org/10.1016/j.ymssp.2012.10.019.
[10] 裴益轩,郭民.滑动平均法的基本原理及应用[J]. 火炮发射与控制学报, 2001, 1(1): 21-23.
PEI Yixuan, GUO Ming. The fundamental principle and application for sliding average method[J]. GUN LAUNCH & CONTROL JOURNAL, 2001, 1(1): 21-23.
[11]   DIGALAKIS V, ROHLICEK J R, OSTENDORF M. ML estimation of a stochastic linear system with the EM algorithm and its application to speech recognition[J]. Speech and Audio Processing, IEEE Transactions on, 1993, 1(4): 431-442.
[12] FEMTO-ST, IEEE PHM 2012 Data Challenge, at: http://www.femto-st.fr/en/Research-departments/AS2M/Research-groups/PHM/IEEE-PHM-2012-Data-challenge.
[13] ZHONG Z W, ZHOU J H, WIN Y N. Correlation analysis of cutting force and acoustic emission signals for tool condition monitoring [C]. Control Conference (ASCC), 2013 9th Asian. IEEE, 2013: 1-6.