基于Wiener过程的电子测量设备性能退化建模与寿命预测
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摘要
目的开展电子测量设备寿命预测,评价其健康状态,提升装备状态监测的准确性,充分发挥装备性能。方法利用电子测量设备长期观测的性能退化数据,基于Wiener过程建立电子测量设备性能退化模型和可靠性模型,并结合环境剖面参数,进行性能退化模型参数估计。结果以某型电子测量设备为例,建立了拟合性较好的性能退化建模,并进行寿命预测。结论该方法降低了电子测量设备在寿命预测过程中的试验成本,提升了寿命预测技术的实践能力,具有一定的工程应用价值。
Objective To predict the life of electronic measuring equipment, evaluate its health state, improve the accuracy of equipment condition monitoring and give full play to the equipment performance. Methods Based on the Wiener process, the performance degradation model and reliability model of the electronic measurement equipment were established, and the model parameters were estimated in combination with the degradation data and the environmental parameters of the electronic measuring equipment. Results With a certain type of electronic measuring equipment as an example, the performance degradation modeling with better fitting was built, and life prediction was carried out. Conclusion This method reduces the test cost of the electronic measuring equipment in the life prediction, improves the practical ability of the life prediction technology, and has certain engineering application value.
Objective To predict the life of electronic measuring equipment, evaluate its health state, improve the accuracy of equipment condition monitoring and give full play to the equipment performance. Methods Based on the Wiener process, the performance degradation model and reliability model of the electronic measurement equipment were established, and the model parameters were estimated in combination with the degradation data and the environmental parameters of the electronic measuring equipment. Results With a certain type of electronic measuring equipment as an example, the performance degradation modeling with better fitting was built, and life prediction was carried out. Conclusion This method reduces the test cost of the electronic measuring equipment in the life prediction, improves the practical ability of the life prediction technology, and has certain engineering application value.
引文
[1]王书锋,王友仁,姜媛媛.Wiener过程性能退化电子产品的剩余寿命预测方法[J].电子测量技术,2014,37(5):17-20.
[2]林震,姜同敏,程永生.阿伦尼斯模型研究[J].电子产品可靠性与环境试验,2005,23(6):12-14.
[3]杨静,王丽霞.爱因斯坦与布朗运动的数学理论[J].西北大学学报(自然科学版),2006,36(1):169-172.
[4]尹慧琳,杨筱菡,陆恒.Wiener过程性能退化产品可靠性评估新Bayes方法[J].同济大学学报(自然科学版),2015,43(8):1234-1238.
[5]范金城,梅长林.数据分析(第二版)[M].北京:科学出版社,2010.
[6]DANIELS H E.Approximating the First Crossing-time Density for a Curved Boundary[J].Bernoulli,1996,2(2):133-143.
[7]LIAO H,TIAN Z.A Framework for Predicting the Remaining Useful Life of a Single Unit under Time-varying Operating Conditions[J].IIE Transactions,2013,45(9):964-980.
[8]司小胜,周东华,胡昌华.带测量误差的非线性退化过程建模与剩余寿命估计[J].自动化学报,2012,38(1):1-12.
[9]马伦,康建设,赵春宇,等.武器装备故障预测建模方法选择研究[J].计算机应用研究,2013,30(7):1929-1938.
[10]谷玉波,贾云献,张英波.基于Gamma退化过程的剩余寿命预测及维修决策优化模型研究[J].轴承,2013(4):44-49.
[11]BIAN L,GEBRAEEL N.Stochastic Methodology for Prognostics under Continuously Varying Environmental Profiles[J].Statistical Analysis&Data Mining,2013,6(3):260-270.
[12]BIAN L,GEBRAEEL N,KHAROUFEH J.Degradation Modeling for Real-Time Estimation of Residual Lifetimes in Dynamic Environments[J].IIE Transactions,2014,47(5):471-486.
[13]TANG L,CHANG D.Reliability Prediction Using Nondestructive Accelerated-degradation Data:Case Study on Power Supplies[J].IEEE Transactions on Reliability,2010,44(4):562-566.
[14]SI X S,WANG W B,HU C H,et al.Remaining Useful Life Estimation-A Review on the Statistical Data Driven Approaches[J].European Journal of Operational Research,2011,213(1):1-14.
[15]彭宇,刘大同,彭喜元.故障预测与健康管理技术综述[J].电子测量与仪器学报,2010,24(1):1-9.
[2]林震,姜同敏,程永生.阿伦尼斯模型研究[J].电子产品可靠性与环境试验,2005,23(6):12-14.
[3]杨静,王丽霞.爱因斯坦与布朗运动的数学理论[J].西北大学学报(自然科学版),2006,36(1):169-172.
[4]尹慧琳,杨筱菡,陆恒.Wiener过程性能退化产品可靠性评估新Bayes方法[J].同济大学学报(自然科学版),2015,43(8):1234-1238.
[5]范金城,梅长林.数据分析(第二版)[M].北京:科学出版社,2010.
[6]DANIELS H E.Approximating the First Crossing-time Density for a Curved Boundary[J].Bernoulli,1996,2(2):133-143.
[7]LIAO H,TIAN Z.A Framework for Predicting the Remaining Useful Life of a Single Unit under Time-varying Operating Conditions[J].IIE Transactions,2013,45(9):964-980.
[8]司小胜,周东华,胡昌华.带测量误差的非线性退化过程建模与剩余寿命估计[J].自动化学报,2012,38(1):1-12.
[9]马伦,康建设,赵春宇,等.武器装备故障预测建模方法选择研究[J].计算机应用研究,2013,30(7):1929-1938.
[10]谷玉波,贾云献,张英波.基于Gamma退化过程的剩余寿命预测及维修决策优化模型研究[J].轴承,2013(4):44-49.
[11]BIAN L,GEBRAEEL N.Stochastic Methodology for Prognostics under Continuously Varying Environmental Profiles[J].Statistical Analysis&Data Mining,2013,6(3):260-270.
[12]BIAN L,GEBRAEEL N,KHAROUFEH J.Degradation Modeling for Real-Time Estimation of Residual Lifetimes in Dynamic Environments[J].IIE Transactions,2014,47(5):471-486.
[13]TANG L,CHANG D.Reliability Prediction Using Nondestructive Accelerated-degradation Data:Case Study on Power Supplies[J].IEEE Transactions on Reliability,2010,44(4):562-566.
[14]SI X S,WANG W B,HU C H,et al.Remaining Useful Life Estimation-A Review on the Statistical Data Driven Approaches[J].European Journal of Operational Research,2011,213(1):1-14.
[15]彭宇,刘大同,彭喜元.故障预测与健康管理技术综述[J].电子测量与仪器学报,2010,24(1):1-9.