基于特征变化率的运行可靠性评估
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摘要
针对传统的基于概率论与数理统计的可靠性理论无法评估单个或者小样本设备可靠性的问题,在比例故障率模型的基础上,综合利用希尔伯特熵等多维特征,考虑设备退化过程中时间尺度的影响,提出了利用特征变化率作为协变量进行运行可靠性评估的新方法。该方法综合利用旋转机械可靠性的平均效应(历史失效数据)和实时特征(个体特征变化率),实现了单个设备运行可靠性的实时评估,并提供了剩余寿命的有效预测,为基于状态的维护提供技术支撑。采用PRONOSTIA平台轴承实验数据验证该模型,并与原方法对比,证明了模型改进的有效性和合理性。
Traditional reliability assessment methods based on probability and mathematical statistics can not fulfil the requirements of reliability evaluation of single bearing or few samples. A novel method of reliability evaluation was proposed with multi-dimensional features such as Hilbert entropy based on proportional hazards model, which utilizes feature change rate as co-ordination considering the influence of time scale. The proposed model realized the reliability assessment of single equipment and the prediction of remaining useful life which is vital to condition-based maintenance with incorporating population characteristics information and real-time feature by historical failure data and individual feature change rate. PRONOSTIA platform bearing experimental data were used to verify the model, and the comparison with the original method proved the effectiveness and rationality of the model improvement.
Traditional reliability assessment methods based on probability and mathematical statistics can not fulfil the requirements of reliability evaluation of single bearing or few samples. A novel method of reliability evaluation was proposed with multi-dimensional features such as Hilbert entropy based on proportional hazards model, which utilizes feature change rate as co-ordination considering the influence of time scale. The proposed model realized the reliability assessment of single equipment and the prediction of remaining useful life which is vital to condition-based maintenance with incorporating population characteristics information and real-time feature by historical failure data and individual feature change rate. PRONOSTIA platform bearing experimental data were used to verify the model, and the comparison with the original method proved the effectiveness and rationality of the model improvement.
引文
[1] 徐永成,徐东,杨拥民,等.舰船动力系统轴承疲劳故障诊断与寿命预测技术分析与研究[C]//2008军民两用维修技术学术研讨会论文集,2008.
[2] Sun Y,Ma L,Mathew J,et al.Mechanical systems hazard estimation using condition monitoring[J].Mechanical Systems and Signal Processing,2006,20(5):1189-1201.
[3] Heng A,Tan A C C,Mathew J,et al.Intelligent condition-based prediction of machinery reliability[J].Mechanical Systems and Signal Processing,2009,23(5):1600-1614.
[4] 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.
[5] 黄洪钟.对常规可靠性理论的批判性评述——兼论模糊可靠性理论的产生、发展及应用前景[J].机械科学与技术,1994,11(3):1-5,47.
[6] 訾佼佼,刘宏昭,蒋喜,等.基于退化量分布的电主轴可靠性评估[J].中国机械工程,2014,25(6):807-812.
[7] 孙闯,何正嘉,张周锁,等.基于状态信息的航空发动机运行可靠性评估[J].机械工程学报,2013,49(6):30-37.
[8] Donoho D,Johnstone I.Adapting to unknown smoothness via wavelet shrinkage[J].Publications of the American Statistical Association,1995,90(432):1200-1224.
[9] 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):21-23.
[10] 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].机械工程学报,2014,50(2):171-186.
[11] Kumar D.Proportional hazards modelling of repairable systems[J].Quality and Reliability Engineering International,2010,11(5):361-369.
[12] Sun Y,Ma L,Mathew J,et al.Mechanical systems hazard estimation using condition monitoring[J].Mechanical Systems and Signal Processing,2006,20(5):1189-1201.
[13] Nectoux P,Gouriveau R,Medjaher K,et al.PRONOSTIA:An experimental platform for bearings accelerated degradation tests[C]//IEEE International Conference on Prognostics and Health Management,PHM12,2012.
[14] Chen B.Reliability estimation for cutting tool based on Logistic regression model[J].Journal of Mechanical Engineering,2011,25(7):2526-2537.
[2] Sun Y,Ma L,Mathew J,et al.Mechanical systems hazard estimation using condition monitoring[J].Mechanical Systems and Signal Processing,2006,20(5):1189-1201.
[3] Heng A,Tan A C C,Mathew J,et al.Intelligent condition-based prediction of machinery reliability[J].Mechanical Systems and Signal Processing,2009,23(5):1600-1614.
[4] 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.
[5] 黄洪钟.对常规可靠性理论的批判性评述——兼论模糊可靠性理论的产生、发展及应用前景[J].机械科学与技术,1994,11(3):1-5,47.
[6] 訾佼佼,刘宏昭,蒋喜,等.基于退化量分布的电主轴可靠性评估[J].中国机械工程,2014,25(6):807-812.
[7] 孙闯,何正嘉,张周锁,等.基于状态信息的航空发动机运行可靠性评估[J].机械工程学报,2013,49(6):30-37.
[8] Donoho D,Johnstone I.Adapting to unknown smoothness via wavelet shrinkage[J].Publications of the American Statistical Association,1995,90(432):1200-1224.
[9] 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):21-23.
[10] 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].机械工程学报,2014,50(2):171-186.
[11] Kumar D.Proportional hazards modelling of repairable systems[J].Quality and Reliability Engineering International,2010,11(5):361-369.
[12] Sun Y,Ma L,Mathew J,et al.Mechanical systems hazard estimation using condition monitoring[J].Mechanical Systems and Signal Processing,2006,20(5):1189-1201.
[13] Nectoux P,Gouriveau R,Medjaher K,et al.PRONOSTIA:An experimental platform for bearings accelerated degradation tests[C]//IEEE International Conference on Prognostics and Health Management,PHM12,2012.
[14] Chen B.Reliability estimation for cutting tool based on Logistic regression model[J].Journal of Mechanical Engineering,2011,25(7):2526-2537.