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| Abstract: |
| The complexity of the actual operating environment of EMU trains and the interaction between the reliability of system components have become a huge challenge for the maintenance scheduling of EMU trains. In response to these problems, the evolution of reliability and failure rate under the influence of environmental factors, failure correlations and economy correlations is analyzed. We assume bogie systems form the EMU train in series. The failure correlation matrix of the bogie systems is modeled. With the lowest total maintenance cost as the optimization objective, a decision-making model for EMU train maintenance is established. A dynamic maintenance strategy is proposed for the model, which can improve maintenance plans efficiently. Artificial bee colony algorithm is applied to further iteratively optimize the threshold parameters in the strategy. The results are calculated and verified by a numerical example. The results show the effectiveness of the maintenance decision model. The dynamic maintenance strategy in this paper is compared with the traditional opportunistic maintenance strategy. The proposed maintenance strategy outperforms the traditional opportunistic maintenance strategy in the numerical example. |
| Key words: Preventive maintenance EMU train Correlation Artificial bee colony algorithm Environmental impact |
| DOI：10.11916/j.issn.1005-9113.22031 |
| Clc Number:TH17 |
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| Descriptions in Chinese: |
| 考虑环境因素和多种相关性的动车组列车维修决策 刘剑坤，蒋祖华 （上海交通大学，机械与动力工程学院，上海 200240） 摘要：动车组实际运行环境复杂，系统部件之间可靠度相互影响，这些都对动车组列车的维修决策产生了巨大挑战。针对上述问题，本文分析了环境因素和故障相关矩阵综合影响下动车组列车可靠度与故障率演化规律。考虑到转向架维护在动车组维护中的重要性，以多个转向架系统的串联表示一个动车组列车，以总维修成本最低为优化目标，综合预防性维修和故障维修两种维修方式，建立动车组列车维修决策模型。并针对模型提出一种动态维修策略，该策略可以通过灵活设置预防性维修阈值以及机会维修阈值提高维修计划的经济性。并运用人工蜂群算法对该策略中的阈值参数进行进一步迭代优化。本文通过一个具体的动车组列车作为算例进行结果的计算和验证，结果证明该维修决策模型的有效性。将动态维修策略与传统的机会维修策略对比，算例分析表明本文提出的策略能够适应复杂环境的变化从而降低维修成本。经过对维修策略模型的敏感性分析，研究了故障相关系数以及成本系数对于整体维修决策的影响。本文为实际复杂环境中运行的动车组列车维修提供决策参考，同时为复杂系统的维修决策及可靠度研究提供了理论支持。 关键词： 铁路运输；预防性维修；相关性；人工蜂群算法；环境影响 |
