基于Wiener过程的万能式断路器附件剩余寿命预测
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摘要
万能式断路器作为一个复杂的机械系统,其操作附件的剩余寿命预测对于维护断路器的可靠性至关重要。为准确掌握操作附件剩余寿命情况,提出了一种基于Wiener过程的万能式断路器操作附件剩余机械寿命预测方法。首先,通过对操作附件动作过程中线圈电流波形的分析选取了动作时间作为性能退化特征量;其次,考虑到断路器操作附件性能退化过程具有线性非单调的特点,采用Wiener过程建立了操作附件的性能退化模型,并利用极大似然估计法对退化模型参数进行估计;然后,基于首达时间的概念建立了剩余寿命预测模型,推导出剩余寿命概率密度函数解析式。最后对安装于万能式断路器上的分励脱扣器和释能电磁铁两种操作附件进行全寿命试验及其剩余寿命预测,预测结果验证了所提方法的有效性。
The conventional circuit breaker is one kind of complex mechanical system. The remaining useful life(RUL) prediction of its operating accessories is essential for maintaining the reliability of the circuit breaker. To accurately grasp this RUL, this article proposes a remaining mechanical life prediction method of operating accessories for the conventional circuit breaker based on Wiener process. First, the action time of operating accessories is selected as the performance degradation feature by analyzing the coil current waveform during the operation process of the operating accessories. The performance degradation process of operating accessories is linear and non-monotonic. Hence, the performance degradation model is established by employing Wiener process. The maximum likelihood estimation method is utilized to estimate the parameters of the degradation model. Then, based on the concept of first hitting time, RUL prediction model is formulated and its probability density function is derived, which realizes the RUL prediction of operating accessories. Finally, the whole life tests and the RUL prediction for the shunt release and the release electromagnet as operating accessories installed in the conventional circuit breaker are carried out. The prediction results demonstrate the effectiveness of the proposed method.
The conventional circuit breaker is one kind of complex mechanical system. The remaining useful life(RUL) prediction of its operating accessories is essential for maintaining the reliability of the circuit breaker. To accurately grasp this RUL, this article proposes a remaining mechanical life prediction method of operating accessories for the conventional circuit breaker based on Wiener process. First, the action time of operating accessories is selected as the performance degradation feature by analyzing the coil current waveform during the operation process of the operating accessories. The performance degradation process of operating accessories is linear and non-monotonic. Hence, the performance degradation model is established by employing Wiener process. The maximum likelihood estimation method is utilized to estimate the parameters of the degradation model. Then, based on the concept of first hitting time, RUL prediction model is formulated and its probability density function is derived, which realizes the RUL prediction of operating accessories. Finally, the whole life tests and the RUL prediction for the shunt release and the release electromagnet as operating accessories installed in the conventional circuit breaker are carried out. The prediction results demonstrate the effectiveness of the proposed method.
引文
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[15] BIAN L, GEBRAEEL N. Computing and updating the first-passage time distribution for randomly evolving degradation signals [J]. IIE Transactions, 2012, 44(11): 974-987.
[16] HUANG Z Y, XU ZH G, KE X J, et al. Remaining useful life prediction for an adaptive skew-Wiener process model [J]. Mechanical Systems and Signal Processing, 2017(87): 294-306.
[17] 魏高乐, 陈志军. 基于多阶段- 随机维纳退化过程的产品剩余寿命预测方法[J]. 科学技术与工程, 2015, 15(26): 27-34.WEI G L, CHEN ZH J. Prediction of residual life of products based on multi-stage stochastic Wiener degradation process [J]. Science Technology and Engineering, 2015, 15(26): 27-34.
[18] 李奎, 段宇, 黄少坡, 等. 基于Wiener过程的交流接触器剩余电寿命预测[J]. 中国电机工程学报, 2018, 38(13): 3978-3986,4039.LI K, DUAN Y, HUANG SH P, et al. Residual electrical life prediction of AC contactor based on the Wiener process [J]. Proceedings of the CSEE, 2018, 38(13): 3978-3986,4039.
[19] 司小胜, 胡昌华. 数据驱动的设备剩余寿命预测理论及应用[M]. 北京: 国防工业出版社, 2016: 88-90.SI X SH, HU CH H. Data-Driven Remaining Useful Life Prediction Theory and Applications for Equipment [M]. Beijing: National Defense Industry Press, 2016: 88-90.
[20] AALEN O, BORGAN ?, GJESSING H. Survival and Event History Analysis: A Process Point of View [M]. New York: Springer, 2008: 387- 400.
[21] LEE M, WHITMORE G A. Threshold regression for survival analysis: modeling event times by a stochastic process reaching a boundary [J]. Statistical Science, 2006, 21(4): 501- 513.
[22] COX D, MILLER H. The Theory of Stochastic Processes [M]. London: Chapman and Hall, 1977: 115-121.
[23] 李建生, 梁军, 贠志皓, 等. 基于电气信息评估设备状态渐进变化的概率分析方法[J]. 电工技术学报, 2013, 28(10): 355-363.LI J SH, LIANG J, MOU ZH H, et al. Probability analysis of progressive changes of equipment state based on electrical information [J]. Transactions of China Electrotechnical Society, 2013, 28(10): 355-363.
[2] PECHT M. Prognostics and Health Management of Electronics [M]. New Jersey: Wiley Online Library, 2008: 35- 46.
[3] 赵广社, 吴思思, 荣海军. 多源统计数据驱动的航空发动机剩余寿命预测方法[J]. 西安交通大学学报, 2017, 51(11): 150-155,172.ZHAO G SH, WU S S, RONG H J. A multi-source statistics data-driven method for remaining useful life prediction of aircraft engine [J]. Journal of Xi′an Jiaotong University, 2017, 51(11): 150-155,172.
[4] 王春雷, 赵琦, 秦孝丽, 等. 基于改进相关向量机的锂电池寿命预测方法[J]. 北京航空航天大学学报, 2018, 44(9): 1998- 2003.WANG CH L, ZHAO Q, QIN X L, et al. Life prediction method of lithium battery based on improved relevance vector machine [J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(9): 1998- 2003.
[5] 胡姚刚, 李辉, 廖兴林, 等. 风电轴承性能退化建模及其实时剩余寿命预测[J]. 中国电机工程学报, 2016, 36(6): 1643-1649.HU Y G, LI H, LIAO X L, et al. Performance degradation model and prediction method of real-time remaining life for wind turbine bearings [J]. Proceedings of the CSEE, 2016, 36(6): 1643-1649.
[6] 万安平, 陈坚红, 盛德仁, 等. 基于多重环境时间相似理论的燃气轮机热通道部件剩余寿命预测方法[J]. 中国电机工程学报, 2013, 33(5): 95-101.WAN AN P, CHEN J H, SHENG D R, et al. Residual life prediction method for gas turbine HGP component based on multi-environmental time similarity theory [J]. Proceedings of the CSEE, 2013, 33(5): 95-101.
[7] SUN F Q, LI X Y, LIAO H T, et al. A Bayesian least- squares support vector machine method for predicting the remaining useful life of a microwave component [J]. Advances in Mechanical Engineering, 2017, 9(1): 1-9.
[8] 杨秋玉, 彭彦卿, 庄志坚, 等. 基于随机模糊理论的高压断路器剩余机械寿命评估[J]. 高压电器, 2016, 52(8): 161-165,171.YANG Q Y, PENG Y Q, ZHUANG ZH J, et al. Residual mechanical life evaluation for high voltage circuit breaker based on random fuzzy theory [J]. High Voltage Apparatus, 2016, 52(8): 161-165,171.
[9] QIAN Y N, YAN R Q, GAO R X. A multi-time scale approach to remaining useful life prediction in rolling bearing [J]. Mechanical Systems and Signal Processing, 2017(83): 549- 567.
[10] SIKORSKA J Z, HODKIEWICZ M, MA L. Prognostic modelling options for remaining useful life estimation by industry [J]. Mechanical Systems and Signal Processing, 2011, 25(5): 1803-1836.
[11] PANDEY M D, YUAN X, VAN NOORTWIJK J. The influence of temporal uncertainty of deterioration on life-cycle management of structures [J]. Structure and Infrastructure Engineering, 2009, 5(2): 145-156.
[12] 李奎, 李正广, 段宇, 等. 基于Gamma过程的交流接触器剩余电寿命仿真预测[J]. 电测与仪表, 2018, 55(16): 105-111.LI K, LI ZH G, DUAN Y, et al. Simulation residual electrical life prediction of AC contactor based on the Gamma process [J]. Electrical Measurement & Instrumentation, 2018, 55(16): 105-111.
[13] 何成刚. 马尔科夫模型预测方法的研究及其应用[D]. 合肥: 安徽大学, 2011.HE CH G. The research of Markov model forecasing method and application [D]. Hefei: Anhui University, 2011.
[14] 胡昌华, 施权, 司小胜, 等. 数据驱动的寿命预测和健康管理技术研究进展[J]. 信息与控制, 2017, 46(1): 72- 82.HU CH H, SHI Q, SI X SH, et al. Data-driven life prediction and health management: State of the art [J]. Information and Control, 2017, 46(1): 72- 82.
[15] BIAN L, GEBRAEEL N. Computing and updating the first-passage time distribution for randomly evolving degradation signals [J]. IIE Transactions, 2012, 44(11): 974-987.
[16] HUANG Z Y, XU ZH G, KE X J, et al. Remaining useful life prediction for an adaptive skew-Wiener process model [J]. Mechanical Systems and Signal Processing, 2017(87): 294-306.
[17] 魏高乐, 陈志军. 基于多阶段- 随机维纳退化过程的产品剩余寿命预测方法[J]. 科学技术与工程, 2015, 15(26): 27-34.WEI G L, CHEN ZH J. Prediction of residual life of products based on multi-stage stochastic Wiener degradation process [J]. Science Technology and Engineering, 2015, 15(26): 27-34.
[18] 李奎, 段宇, 黄少坡, 等. 基于Wiener过程的交流接触器剩余电寿命预测[J]. 中国电机工程学报, 2018, 38(13): 3978-3986,4039.LI K, DUAN Y, HUANG SH P, et al. Residual electrical life prediction of AC contactor based on the Wiener process [J]. Proceedings of the CSEE, 2018, 38(13): 3978-3986,4039.
[19] 司小胜, 胡昌华. 数据驱动的设备剩余寿命预测理论及应用[M]. 北京: 国防工业出版社, 2016: 88-90.SI X SH, HU CH H. Data-Driven Remaining Useful Life Prediction Theory and Applications for Equipment [M]. Beijing: National Defense Industry Press, 2016: 88-90.
[20] AALEN O, BORGAN ?, GJESSING H. Survival and Event History Analysis: A Process Point of View [M]. New York: Springer, 2008: 387- 400.
[21] LEE M, WHITMORE G A. Threshold regression for survival analysis: modeling event times by a stochastic process reaching a boundary [J]. Statistical Science, 2006, 21(4): 501- 513.
[22] COX D, MILLER H. The Theory of Stochastic Processes [M]. London: Chapman and Hall, 1977: 115-121.
[23] 李建生, 梁军, 贠志皓, 等. 基于电气信息评估设备状态渐进变化的概率分析方法[J]. 电工技术学报, 2013, 28(10): 355-363.LI J SH, LIANG J, MOU ZH H, et al. Probability analysis of progressive changes of equipment state based on electrical information [J]. Transactions of China Electrotechnical Society, 2013, 28(10): 355-363.