Full Text:   <1919>

Summary:  <1708>

CLC number: TH17

On-line Access: 2015-08-04

Received: 2014-10-15

Revision Accepted: 2015-07-07

Crosschecked: 2015-07-09

Cited: 1

Clicked: 3962

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jian Zhang

http://orcid.org/0000-0002-8411-8131

Ji-en Ma

http://orcid.org/0000-0001-6970-3634

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE A 2015 Vol.16 No.8 P.597-606

http://doi.org/10.1631/jzus.A1400311


Optimal condition-based maintenance strategy under periodic inspections for traction motor insulations


Author(s):  Jian Zhang, Ji-en Ma, Xiao-yan Huang, You-tong Fang, He Zhang

Affiliation(s):  1College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   jienma@126.com

Key Words:  Traction motor insulation, Condition-based maintenance (CBM), Preventive maintenance (PM), Shock


Share this article to: More |Next Article >>>

Jian Zhang, Ji-en Ma, Xiao-yan Huang, You-tong Fang, He Zhang. Optimal condition-based maintenance strategy under periodic inspections for traction motor insulations[J]. Journal of Zhejiang University Science A, 2015, 16(8): 597-606.

@article{title="Optimal condition-based maintenance strategy under periodic inspections for traction motor insulations",
author="Jian Zhang, Ji-en Ma, Xiao-yan Huang, You-tong Fang, He Zhang",
journal="Journal of Zhejiang University Science A",
volume="16",
number="8",
pages="597-606",
year="2015",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1400311"
}

%0 Journal Article
%T Optimal condition-based maintenance strategy under periodic inspections for traction motor insulations
%A Jian Zhang
%A Ji-en Ma
%A Xiao-yan Huang
%A You-tong Fang
%A He Zhang
%J Journal of Zhejiang University SCIENCE A
%V 16
%N 8
%P 597-606
%@ 1673-565X
%D 2015
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1400311

TY - JOUR
T1 - Optimal condition-based maintenance strategy under periodic inspections for traction motor insulations
A1 - Jian Zhang
A1 - Ji-en Ma
A1 - Xiao-yan Huang
A1 - You-tong Fang
A1 - He Zhang
J0 - Journal of Zhejiang University Science A
VL - 16
IS - 8
SP - 597
EP - 606
%@ 1673-565X
Y1 - 2015
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1400311


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.

This paper proposes a model for the optimization of maintenance decisions under both fatigue and random shocks. The objective is to derive the optimal inspection schedule and preventive maintenance time for systems with two operational states and a functional failure state.

基于周期性监测的牵引电机绝缘最优视情维修决策

目的:充分利用寿命信息,并考虑冲击效应对维修策略的影响,提出符合牵引电机绝缘运行特征的最优视情维修策略。
方法:1. 采用正常状态、潜在故障状态与功能故障状态描述牵引电机绝缘运行状况;2. 应用泊松过程描述随机高电压冲击;3. 以单位时间的运行成本为目标函数,以监测间隔和预防性维修寿命阈值为优化变量,建立最优视情维修决策 模型。
结论:该模型能显著降低牵引电机绝缘功能故障的风险,同时降低单位时间运行成本。

关键词:牵引电机绝缘;视情维修;预防性维修;冲击

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[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.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2022 Journal of Zhejiang University-SCIENCE