基于变权模糊综合法与人工神经网络的地铁车辆转向架系统健康状态评价
张磊1韩斌2樊茜琪2
Health Status Evaluation of Metro Vehicle Bogie System Based on Variable Weight Fuzzy Synthesis Method and Artificial Neural Network
ZHANG Lei1Han Bin2FAN Qianqi2
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作者信息:1.上海市轨道交通结构耐久与系统安全重点实验室， 201804， 上海
2.同济大学铁道与城市轨道交通研究院， 201804， 上海
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Affiliation:1.Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, 201804, Shanghai, China
2.Institute of Rail Transit, Tongji University, 201804, Shanghai, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2024.02.033
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中图分类号/CLCN:U270.33
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栏目/Col:研究报告
摘要:
［目的］相较于现代地铁车辆普遍采用的计划修和故障修的维修模式，状态修可以在优化维修策略和成本的同时，把握车辆系统的健康状态和运行情况。为实现状态修，需对车辆系统健康状态进行准确评价。［方法］以地铁车辆转向架系统为对象，对其健康状态的评价方法进行深入研究。依据转向架系统组成具有层次性的特点，以劣化度为依据，将层次分析法和变权理论相结合，根据模糊综合评判思路，建立基于劣化度的地铁车辆转向架健康状态变权模糊综合评价模型，提出建模流程；通过选取的地铁车辆转向架系统健康状态评价指标对其进行健康状态评估，获得样本数据；利用样本数据对BP神经网络、支持向量机和随机森林三种不同的人工神经网络进行训练，利用实际测试数据判断三种不同类型人工神经网络的评价效果。 ［结果及结论］随机森林模型对地铁车辆转向架系统健康状态的识别能力最强，可实现对地铁车辆转向架系统的智能化健康评估。
Abstracts:
［Objective］ In contrast to the planned and faulty maintenance modes commonly used in modern metro vehicles, condition-based repair can capture the health status and operation condition of the vehicle system while optimizing maintenance strategies and costs. To realize condition-based repair, the health status of vehicle system needs to be accurately evaluated. ［Method］ Focusing on metro vehicle bogie system as object, the evaluation method of its health status is studied in-depth. Based on the hierarchical characteristics of bogie system composition, with reference to the degree of deterioration, combining the analytical hierarchy process method and variable weight theory are combined, to establish the variable weight fuzzy comprehensive evaluation model of metro vehicle bogie based on deterioration degree according to using the idea of fuzzy comprehensive judgment, and the modelling process is proposed. By evaluating the health status of the selected metro vehicle bogie system with the health status evaluation indexes, sample data is obtained and used to train three different artificial neural networks, namely, BP (back propagation) neural network, support vector machine and random forest. Then, the evaluation performance of the three different types of artificial neural networks is judged with actual test data. ［Result & Conclusion］ The random forest model has the strongest ability to identify the health status of the metro vehicle bogie system and realizes the intelligent health assessment of the metro bogie system.
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