基于FOA-BP神经网络模型的城市轨道交通列车车门故障预诊断
温凯越1仇维斌2丁先泽1欧红香1
Prediction of Urban Rail Transit Train Door Faults Based on FOA-BP Neural Network Model
WEN Kaiyue1QIU Weibin2DING Xianze1OU Hongxiang1
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作者信息:1.常州大学安全科学与工程学院， 213164， 常州
2.无锡学院交通控制工程系， 214105， 无锡
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Affiliation:1.School of Environmental and Safety Engineering, Changzhou University, 213164, Changzhou, China
2.School of Transportation and Vehicle Engineering, Wuxi University, 214105, Wuxi, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2025.05.048
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中图分类号/CLCN:U270.386
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栏目/Col:车辆制造与列车控制
摘要:
［目的］为了在城市轨道交通（以下简称“城轨”）列车车门发生故障前展开预防性维修，避免人员和财产受到损害，保证城轨列车的运行安全性，有必要研究城轨列车车门故障的预诊断。［方法］以城轨列车车门发生故障前的异常电流信号作为研究对象，设计了一种FIR（有限冲激响应）滤波器，对采集到的城轨列车车门电流信号数据进行数据滤波与量纲一化处理；通过FOA（果蝇优化算法）-BP（反向传播）神经网络模型，对量纲一化后不同车门的关门状态学习样本数据进行学习训练，并输出测试结果；对比分析FOA-BP与BP神经网络模型训练后的输出结果。［结果及结论］采用FIR滤波器＋汉宁窗函数法能够有效去除高频噪音的干扰，保留能够正确反映电流变化趋势的低频信号部分；相比于传统BP神经网络模型，采用FOA-BP神经网络模型进行训练，具有训练方法简洁、训练时间短、诊断精度大幅提高等优点；FOA-BP神经网络模型的真实输出值与期望输出值误差小于1%，能够满足城轨列车车门故障精准诊断的需求。
Abstracts:
［Objective］In order to carry out preventive maintenance before urban rail transit (URT) train doors fail, avoid personnel and property damage, and ensure the operation safety of URT trains, it is necessary to study the pre-diagnosis of URT train door failures. ［Method］Taking the abnormal current signal of URT train door before failure as the research object, a FIR (finite impulse response) filter is designed to filter and dimensionally normalize the collected URT train door current signal data; the FOA (fruit fly optimization algorithm)-BP (back propagation) neural network model is used to train the closed state learning sample data of different doors after dimensional normalization, and output test results; FOA-BP results and output results after BP neural network models training are compared and analyzed. ［Result & Conclusion］The FIR filter+Hanning Window function method can effectively remove the interference of high-frequency noise and retain the low-frequency signal part that can correctly reflect the current change trend. Compared with the traditional BP neural network model, the FOA-BP model has the advantages of simple training method, short training time and greatly improved diagnostic accuracy. The error between the actual output value and the expected FOA-BP model output value is less than 1%, satisfying accurate diagnosis needs of URT train door faults.
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