基于Dropout法优化的BP神经网络地铁列车塞拉门故障检测
郭井宽
BP Neural Fault Detection of Network Metro Train Sliding Plug Door Based on DropoutOptimization
GUO Jingkuan
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作者信息:中国电气装备集团科学技术研究院有限公司， 200436， 上海
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Affiliation:China Electrical Equipment Group Science and Technology Research Institute Co., Ltd., 200436, 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.2022.12.007
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中图分类号/CLCN:U270.38+6
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栏目/Col:学术专论
摘要:
对正常、下挡销故障、压轮故障3种状态下的地铁列车塞拉门电机电流进行了时域分析，发现预处理后的3种状态电机电流在时域分布上有明显差异。在此基础上，提取3种状态下的电机电流并筛选合适的时域特征参数，将之与BP(后向传播)神经网络相结合，建立了1种基于Dropout法优化的BP神经网络地铁列车塞拉门故障检测模型，实现了对地铁列车塞拉门下挡销及压轮故障的检测。基于实际案例数据的测试结果表明：该模型可在一定程度上减少过拟合现象的发生，能有效检测出塞拉门下挡销及压轮的故障，其故障检测精度较高。
Abstracts:
A time-domain analysis of the motor current of metro sliding plug door is carried out in three states of normal, lower gear pin failure and pressure wheel faults. It is found that the motor currents in the three states have obvious differences in time-domain distribution after preprocessing. On this basis, the motor current in three states is extracted and appropriate time-domain feature parameters are screened, which are then combined with BP (back propagation) neural network. Thus a BP neural network model with Dropout optimization for fault detection of metro sliding plug doors is proposed, and the detection of lower gear pin and pressure wheel faults of the metro sliding plug door is implemented. Test results based on actual case data show that the model can reduce the occurrence of overfitting situation to certain extent and effectively detect the lower gear pin and pressure wheel faults of plug door with high accuracy.
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