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Failure mechanisms discrimination and life prediction of safety relay(PDF)


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Failure mechanisms discrimination and life prediction of safety relay
SUN Yong-kui1 ZHANG Yu-zhuo1 XU Chao-fan1 CAO Yuan2
1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China; 2. National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing 100044, China
safety relay life prediction failure mechanism discrimination principal component analysis Euclidean distance grey theory
The failure mechanisms of a safety relay were analysed and divided into three failure modes. The contact resistance, closing time, super-path time, bounce time, releasing time and arc time were used as failure characteristic parameters, and the wavelet transform and moving average methods were utilized to achieve denoising. Aimed to the possibility of correlation among characteristic parameters, the principal component analysis method was used to achieve the dimension reduction for the multi-dimensional characteristic parameters to correlate the characteristic parameters. The average gradient value of principal components was defined. The failure modes of 18 sets of contacts were discriminated by using the Euclidean distance discrimination criterion. The characteristic parameters of contacts were analysed using the Fisher discrimination method. The characteristic parameter significantly reflected the features of a safety relay’s life was selected as the prediction parameter. A grey prediction model was established to predict the safety relay’s life, and the prediction effect of the model was verified. Research result indicates that after denoising, the characteristic parameters have good smoothness, high signal-to-noise ratios, and low root mean square errors, indicating that a good denoising effect is achieved. The failure modes of 18 sets of contacts are discriminated with an accuracy of 83.3% using the Euclidean distance discrimination criterion. The super-path time has the largest Fisher discrimination function value of 8.2, indicating that the super-path time is an important parameter to represent the life of a safety relay. The actual lifetime of the safety relay is 122 000 times, the prediction life is 116 000 times based on the grey model, and the relative error of the prediction life is 4.9%, indicating that the proposed life prediction model has high accuracy and good feasibility. 5 tabs, 8 figs, 30 refs.


[1] 卢耀辉,向鹏霖,曾 京,等.高速列车转向架构架动应力计算与疲劳全寿命预测[J].交通运输工程学报,2017,17(1):62-70. LU Yao-hui, XIANG Peng-lin, ZENG Jing, et al. Dynamic stress calculation and fatigue whole life prediction of bogie frame for high-speed train[J]. Journal of Traffic and Transportation Engineering, 2017, 17(1): 62-70.(in Chinese)
[2] YIN Jia-teng, ZHAO Wen-tian. Fault diagnosis network design for vehicle on-board equipments of high-speed railway: a deep learning approach[J]. Engineering Applications of Artificial Intelligence, 2016, 56: 250-259.
[3] 翟国富,王淑娟,许 峰,等.基于超程时间和吸合时间建模的继电器双变量寿命预测方法的研究[J].中国电机工程学报,2002,22(7):76-80. ZHAI Guo-fu, WANG Shu-juan, XU Feng, et al. Research on double-variable life forecasting based on model-building of super-path time and pick-up time for relays[J]. Proceedings of the CSEE, 2002, 22(7): 76-80.(in Chinese)
[4] 李 华,孙东旺,贺鹏举,等.基于超程时间回归模型的继电器寿命预测方法[J].电工技术学报,2013,28(增2):414-417. LI Hua, SUN Dong-wang, HE Peng-ju, et al. The method of relay life prediction based on the regression model of super-path time[J]. Transactions of China Electrotechnical Society, 2013, 28(S2): 414-417.(in Chinese)
[5] 李玲玲,张士暖,李志刚,等.基于粗糙集理论和生命初态信息的继电器寿命预测方法[J].电工技术学报,2016,31(18):46-52. LI Ling-ling, ZHANG Shi-nuan, LI Zhi-gang, et al. The life prediction method of relay based on rough set theory and relay’s initial life information[J]. Transactions of China Electrotechnical Society, 2016, 31(18): 46-52.(in Chinese)
[6] 李志刚,刘伯颖,李玲玲,等.基于小波包变换及RBF神经网络的继电器寿命预测[J].电工技术学报,2015,30(14):233-240. LI Zhi-gang, LIU Bo-ying, LI Ling-ling, et al. Life prediction of relay based on wavelet packet transform and RBF neural network[J]. Transactions of China Electrotechnical Society, 2015, 30(14): 233-240.(in Chinese)
[7] ZHOU Xin, ZOU Lian, BRIGGS R. Prognostic and diagnostic technology for DC actuated contactors and motor starters[J]. IEICE Transactions on Electronics, 2009, 92(8): 1045-1051.
[8] 张菲菲,李志刚.基于BP神经网络的继电器剩余寿命预测[J].电器与能效管理技术,2012(1):11-14. ZHANG Fei-fei, LI Zhi-gang. Remaining lifetime prediction of relay based on BP neural network[J]. Electrical and Energy Management Technology, 2012(1): 11-14.(in Chinese)
[9] 李文华,周露露,王立国,等.基于统计分析的继电器贮存寿命神经网络预测[J].航天控制,2016,34(6):79-84. LI Wen-hua, ZHOU Lu-lu, WANG Li-guo, et al. Neural network prediction of relay storage life based on statistical analysis[J]. Aerospace Control, 2016, 34(6): 79-84.(in Chinese)
[10] 王淑娟,余 琼,翟国富.电磁继电器接触失效机理判别方法[J].电工技术学报,2010,25(8):38-44. WANG Shu-juan, YU Qiong, ZHAI Guo-fu. Discrimination method of contact failure mechanisms for electromagnetic apparatus[J]. Transactions of China Electrotechnical Society, 2010, 25(8): 38-44.(in Chinese)
[11] LEUNG C H, LEE A. Electric contact materials and their erosion in automotive DC relays[C]∥IEEE. Proceedings of the 37th IEEE Holm Conference on Electrical Contacts. New York: IEEE, 1991: 114-121.
[12] HAMMERSCHMIDT M, NEUHAUS A R, RIEDER W F. The effects of material transfer in relays diagnosed by force and/or voltage measurement[J]. IEEE Transactions on Components and Packaging Technologies, 2004, 27(1): 12-18.
[13] HASEGAWA M, NIIZUMA K, MIZUKOSHI H, et al. Material transfer characteristic of silver contacts under resistance DC load conditions[C]∥IEEE. Proceedings of the 39th IEEE Holm Conference on Electrical Contacts. New York: IEEE, 1993: 275-281.
[14] 马 跃.航天继电器失效机理与寿命预测方法的研究[D].哈尔滨:哈尔滨工业大学,2013. MA Yue. Reserch on failure mechanism and life prediction method for aerospace relays[D]. Harbin: Harbin Institute of Technology, 2013.(in Chinese)
[15] MUÑOZ M, ARGOUL P, FARGES F. Continuous Cauchy wavelet transform analyses of EXAFS spectra: a qualitative approach[J]. American Mineralogist, 2015, 88(4): 694-700.
[16] HE Hai-bo, STARZYK J A. A self-organizing learning array system for power quality classification based on wavelet transform[J]. IEEE Transactions on Power Delivery, 2006, 21(1): 286-295.
[17] 刘 军,薛 蓉,王得发.基于小波变换的电力变压器继电保护研究[J].电子测量技术,2016,39(1):22-26. LIU Jun, XUE Rong, WANG De-fa. Research on power transformer relay protection based on wavelet transform[J]. Electronic Measurement Technology, 2016, 39(1): 22-26.(in Chinese)
[18] 文鸿雁.基于小波理论的变形分析模型研究[D].武汉:武汉大学,2004. WEN Hong-yan. Research on deformation analysis model based on wavelet transform theory[D]. Wuhan: Wuhan University, 2004.(in Chinese)
[19] 陈 强,黄声享,王 韦.小波降噪效果评价的另一指标[J].测绘信息与工程,2008,33(5):13-14. CHEN Qiang, HUANG Sheng-xiang, WANG Wei. An evaluation indicator of wavelet denosing[J]. Journal of Geomatics, 2008, 33(5): 13-14.(in Chinese)
[20] ZHAO Ke, UPADHYAYA B R. Model based approach for fault detection and isolation of helical coil steam generator systems using principal component analysis[J]. IEEE Transactions on Nuclear Science, 2006, 53(4): 2343-2352.
[21] CHO J H, LEE J M, CHOI S W, et al. Sensorfault identification based on kernel principal component analysis[C]∥IEEE. Proceedings of the 2004 IEEE International Conference on Control Applications. New York: IEEE, 2004: 1223-1228.
[22] LUAN Xiao, FANG Bin, LIU Ling-hui, et al. Extracting sparse error of robust PCA for face recognition in the presence of varying illumination and occlusion[J]. Pattern Recognition, 2014, 47(2): 495-508.
[23] 律方成,金 虎,王子建,等.基于主成分分析和多分类相关向量机的GIS局部放电模式识别[J].电工技术学报,2015,30(6):225-231. LU Fang-cheng, JIN Hu, WANG Zi-jian, et al. GIS partial discharge pattern recognition based on principal component analysis and milticlass relevance vector machine[J]. Transactions of China Electrotechnical Society, 2015, 30(6): 225-231.(in Chinese)
[24] NAKAYAMA Y, PUTRI S P, BAMBA T, et al. Metabolic distance estimation based on principle component analysis of metabolic turnover[J]. Journal of Bioscience and Bioengineering, 2014, 118(3): 350-355.
[25] 韩 敏,张占奎.基于改进核主成分分析的故障检测与诊断方法[J].化工学报,2015,66(6):2139-2149. HAN Min, ZHANG Zhan-kui. Fault detection and diagnosis method based on modified kernel principal component analysis[J]. CIESC Journal, 2015, 66(6): 2139-2149.(in Chinese)
[26] 戴 欢,吴小俊.基于图像欧氏距离的人脸描述和识别方法[J].江南大学学报:自然科学版,2009,8(1):20-23. DAI Huan, WU Xiao-jun. Face representation and recognition based on image Euclidean distance[J]. Journal of Jiangnan University: Natural Science Edition, 2009, 8(1): 20-23.(in Chinese)
[27] ZHAO Chun-hui, GAO Fu-rong. A nested-loop Fisher discriminant analysis algorithm[J]. Chemometrics and Intelligent Laboratory Systems, 2015, 146: 396-406.
[28] ZHOU Zhi-jing, CHEN Jin-liang, SHEN Bei-bei, et al. A trajectory prediction method based on aircraft motion model and grey theory[C]∥IEEE. Advanced Information Management, Communicates, Electronic and Automation Control Conference. New York: IEEE, 2016: 1523-1527.
[29] LEE J Y. Applying grey theory in predicting the arsenic contamination of groundwater in historical blackfoot disease territory[C]∥IEEE. Ninth International Conference on Natural Computation. New York: IEEE, 2013: 1124-1128.
[30] 翟 军,盛建明,冯英俊.MGM(1, n)灰色模型其应用[J]. 系统工程理论与实践,1997,5(1):109-113. ZHAI Jun, SHENG Jian-ming, FENG Ying-jun. The grey model MGM(1,n)and its application[J]. Systems Engineering—Theory and Practice, 1997, 5(1): 109-113.(in Chinese)


Last Update: 2018-07-14