Abstract: Insulation failure is a crucial failure mode of traction motors. Insulation deteriorates under both fatigue load and shock. This paper focuses on proposing an optimal insulation condition-based maintenance strategy. By combining the information obtained from periodic inspections with historic life information, an integrated model of time-based maintenance and condition-based model is proposed, in which random shocks following Poisson process are also taken into account. In this model we define that insulation has three states: normal state, latent failure state, and functional failure state. Normal state and latent failure state differ in their operating cost, proneness to functional failure, and survival probability under extreme shocks. preventive maintenance (PM) will be launched if an inspection result exceeds the threshold or if the operating time reaches the critical age. One operating cycle ends as soon as a preventive maintenance or a corrective maintenance is completed. Moreover, an optimization model is established, which takes minimal cost per unit time as the objective, and inspection interval and critical age as the optimization variables. Finally, a numerical example illustrates the analytic results.

基于周期性监测的牵引电机绝缘最优视情维修决策

[1]Ahmad, R., Kamaruddin, S., 2012. An overview of time-based and condition-based maintenance in industrial application. Computer & Industrial Engineering, 63(1):135-149.

[2]Asadzadeh, S.M., Azadeh, A., 2014. An integrated systemic model for optimization of condition-based maintenance with human error. Reliability Engineering & System Safety, 124:117-131.

[3]Caballé, N.C., Castro, I.T., Pézrez, C.J., et al., 2015. A condition-based maintenance of a dependent degradation-threshold-shock model in a system with multiple degradation processes. Reliability Engineering & System Safety, 134:98-109.

[4]Castro, I.T., 2013. An age-based maintenance strategy for a degradation-threshold-shock-model for a system subjected to multiple defects. Asia-Pacific Journal of Operational Research, 30(05):1350016-1350029.

[5]Castro, I.T., Caballé, N.C., Pérez, N.C., 2015. A condition-based maintenance for a system subject to multiple degradation processes and external shocks. International Journal of Systems Science, 46(9):1692-1704.

[6]Chen, J.Y., Li, Z.H., 2008. An extended extreme shock maintenance model for a deteriorating system. Reliability Engineering & System Safety, 93(8):1123-1129.

[7]Fang, Y.T., 2011. On China’s high-speed railway technology. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 12(12):883-884.

[8]Gao, Y.C., Feng, Y.X., Tan, J.R., 2014. Multi-principle preventive maintenance: a design-oriented scheduling study for mechanical systems. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 15(11):862-872.

[9]Gebraeel, N.Z., Lawley, M.A., Li, R., et al., 2005. Residual life distributions from component degradation signals: a Bayesian approach. IIE Transactions, 37(6):543-557.

[10]Hu, Q.Y., 1995. The optimal replacement of Markov deteriorative under stochastic shocks. Microelectronics Reliability, 35(1):27-31.

[11]Lam, Y., 2009. A geometric process δ-shock maintenance model. IEEE Transactions on Reliability, 58(2):389-396.

[12]Lin, Y.H., Li, Y.F., Zio, E., 2014. Multi-state physics model for the reliability assessment of a component under degradation processes and random shocks. ESREL, Amsterdam, the Netherlands, p.1-7.

[13]Lin, Y.H., Li, Y.F., Zio, E., 2015. Integrating random shocks into multi-state physics models of degradation processes for component reliability assessment. IEEE Transactions on Reliability, 64(1):154-166.

[14]Liu, B.Y., Fang, Y.T., Wei, J.X., et al., 2007. Inspection-replacement policy of system under predictive maintenance. Journal of Zhejiang University-SCIENCE A, 8(3):495-500.

[15]Lu, X.F., Liu, M., 2014. Hazard rate function in dynamic environment. Reliability Engineering & System Safety, 130:50-60.

[16]Ma, H.Z., 2008. Motor State Monitoring and Fault Diagnosis. China Machine Press, Beijing, China, p.354-370 (in Chinese).

[17]Montoro-Cazorla, D., Pérez-Ocón, R., 2014. A reliability system under different types of shock governed by a Markovian process and maintenance policy K. European Journal of Operational Research, 235(3):636-642.

[18]Panagiotidou, S., Tagaras, G., 2008. Evalution of maintenance policies for equipment subject to quality shifts and failures. International Journal of Production Research, 46(20):5761-5779.

[19]Panagiotidou, S., Tagaras, G., 2010. Statistical process control and condition based maintenance: a meaningful relationship through data sharing. Production and Operations Management, 19(2):156-171.

[20]Panagiotidou, S., Tagaras, G., 2012. Optimal integrated process control and maintenance under general deterioration. Reliability Engineering & System Safety, 104:58-70.

[21]Shi, H., Zeng, J.C., 2014. Preventive maintenance strategy based on life prediction. Computer Integrated Manufacturing Systems, 20(5):1133-1140 (in Chinese).

[22]Tang, D.Y., Makis, V., Jafari, L., et al., 2015. Optimal maintenance policy and residual life estimation for a slowly degrading system subject to condition monitoring. Reliability Engineering & System Safety, 134:198-207.

[23]Tang, Y.Y., Lam, Y., 2006. A δ-shock maintenance model for deteriorating system. European Journal of Operational Research, 168(2):541-556.

[24]van Noortwijk, J.M., 2009. Survey of the application of gamma processes in maintenance. Reliability Engineering & System Safety, 94(1):2-21.

[25]Wang, Z., Huang, H.Z., Li, Y., et al., 2011. An approach to reliability assessment under degradation and shock process. IEEE Transaction of Reliability, 60(4):852-863.

[26]Yang, Y., Klutke, G.A., 2000. Lifetime-characteristics and inspection schemes for levy processes. IEEE Transactions on Reliability, 49(4):377-382.

[27]Yao, Y.Z., Meng, C., Wang, C., et al., 2013. Optimal preventive maintenance policies for equipment under condition monitoring. Computer Integrated Manufacturing Systems, 19(12):2968-2975 (in Chinese).

[28]Yin, H., Zhang, G.J., Zhu, H.P., et al., 2015. An integrated model of statistical process control and maintenance based on the delayed monitoring. Reliability Engineering & System Safety, 133:323-333.

[29]Zhou, K., Wu, G.N., Deng, T., et al., 2006. Aging time effect on PD and space charge behavior in magnet wire under high PWM voltage. IEEE International Symposium on Electrical Insulation, Toronto, Canada, p.159-162.