基于集成深度学习的转辙机故障诊断研究
李雪枝1杨勇豪1汪旭雷1欧志龙2皮金龙2
Research on Switch Machine Fault Diagnosis Based on Integrated Deep Learning
LI Xuezhi1YANG Yonghao1WANG Xulei1OU Zhilong2PI Jinlong2
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作者信息:1.成都地铁运营有限公司， 610081， 成都
2.成都交控科技有限公司， 610041， 成都
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Affiliation:1.Chengdu Metro Operation Co., Ltd., 610081, Chengdu, China
2.Chengdu Jiaokong Technology Co., Ltd., 610041, Chengdu, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2024.04.049
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中图分类号/CLCN:U213.6+1
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栏目/Col:产学研视窗
摘要:
［目的］为了能够充分利用故障日志数据诊断转辙机故障，提出了基于集成学习算法的道岔转辙机故障诊断方法。［方法］通过分析转辙机故障文本数据，并结合专家经验，建立了两级故障诊断思路；将故障文本数据预处理为机器能够识别的数据，作为故障诊断模型输入数据；介绍了基于AdaBoost集成学习法的CNN（卷积神经网络）-LSTM（长短期记忆网络）故障诊断模型的原理和方法。［结果及结论］试验结果表明，在数据类别不平衡或者样本数量有限的情况下，采用CNN-LSTM模型能够有效提高故障诊断的准确率；与其他故障诊断模型相比，CNN-LSTM模型性能更好；所提出方法具有有效性，能够满足应用场景准确率要求。
Abstracts:
［Objective］ To make full use of fault log data for diagnosing switch machine faults, a fault diagnosis method based on integrated deep learning is proposed. ［Method］ By analyzing the textual data of switch machine faults and combining expert experiences, a two-level fault diagnosis approach is established. The fault text data is preprocessed into machine-readable data, serving as input data for the fault diagnosis model. The principle and method of the CNN-LSTM fault diagnosis model based on the AdaBoost integrated deep learning method are introduced. ［Result & Conclusion］ Experimental results demonstrate that under conditions of data class imbalance or limited sample size, the CNN-LSTM model can effectively improve the accuracy of fault diagnosis. Compared with other fault diagnosis models, the CNN-LSTM model performs better. The proposed method is effective and can meet the accuracy requirements of application scenarios.
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