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组合式同相供电装置维修决策建模及优化

刘航,  李群湛,  郭锴

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文章导航 > 西南交通大学学报  > 2017 >  30(2): 355-362.
刘航, 李群湛, 郭锴. 组合式同相供电装置维修决策建模及优化[J]. 西南交通大学学报, 2017, 30(2): 355-362. doi: 10.3969/j.issn.0258-2724.2017.02.020
引用本文: 刘航, 李群湛, 郭锴. 组合式同相供电装置维修决策建模及优化[J]. 西南交通大学学报, 2017, 30(2): 355-362. doi: 10.3969/j.issn.0258-2724.2017.02.020
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LIU Hang, LI Qunzhan, GUO Kai. Maintenance Decision Modeling and Optimization Based on Markov Process for Combined Co-Phase Power Supply Equipment[J]. Journal of Southwest Jiaotong University, 2017, 30(2): 355-362. doi: 10.3969/j.issn.0258-2724.2017.02.020
Citation: LIU Hang, LI Qunzhan, GUO Kai. Maintenance Decision Modeling and Optimization Based on Markov Process for Combined Co-Phase Power Supply Equipment[J]. Journal of Southwest Jiaotong University, 2017, 30(2): 355-362. doi: 10.3969/j.issn.0258-2724.2017.02.020
shu

组合式同相供电装置维修决策建模及优化

doi: 10.3969/j.issn.0258-2724.2017.02.020
基金项目: 

中国铁路总公司重点科技项目(2015J005-A)

详细信息
    作者简介:

    刘航(1980-),男,博士研究生,研究方向为牵引供电系统优化,电话:15811159211,E-mail:249449722@qq.com

Maintenance Decision Modeling and Optimization Based on Markov Process for Combined Co-Phase Power Supply Equipment

    摘要
  • 摘要: 为了保障同相供电系统工程化的可靠性和经济性，研究了电气化铁路组合式同相供电装置的维修决策建模及优化问题。首先，根据装置构建方案和运行模式，分析了新型组合式同相供电装置的可靠性；其次，通过装备维修性及劣化性能分析，提出了一种基于Markov过程的维修决策模型及基于最优维修策略的迭代算法；最后，以维修费用最低为目标，综合考虑系统的状态转移概率、维修时间等因素，通过模型计算了状态维修的最佳时机。算例显示，若将系统劣化状态由优到劣分为6类，根据每类状态的维修时间及费用，可以计算出在第5类劣化状态时进行维修具有最高的经济性，仅为定期更换维修费用的50%。

     

    Abstract: To ensure reliability and economic efficiency in the engineering application of co-phase power supply systems, maintenance decision modeling and optimization for combined co-phase power supply units used in electrified railways was studied. Firstly, according to construction plan and operational mode of units, the reliability of new combined co-phase power supply unites were analyzed. Secondly, maintenance decision modeling based on Markov process and iterative algorithm based on optimal maintenance strategy were presented by analyzing the maintainability and degradation of units. Finally, let the lowest maintenance costs as the target and take a overall consideration on the factors such as state transition probability of system and maintenance time, the best condition-based maintenance time was calculated using this model. The calculation examples show that if system degradation states is categorized into 6 classes from good to bad, according to the maintenance time and cost of each state, maintenance in the fifth degradation status has the highest economic efficiency, and the cost is only half the regular replacement cost of failed components.

     

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出版历程
  • 收稿日期:  2016-03-05
  • 刊出日期:  2017-04-25

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