| 王瑞峰,李 扬.基于 1DCNN-BiLSTM 组合模型的
S700K 转辙机故障诊断[J].电子测量与仪器学报,2022,36(11):193-200 |
| 基于 1DCNN-BiLSTM 组合模型的
S700K 转辙机故障诊断 |
| Fault diagnosis of S700K switch machine based on1DCNN-BiLSTM hybrid model |
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| DOI： |
| 中文关键词: 1DCNN BiLSTM S700K 转辙机 故障诊断 |
| 英文关键词:1DCNN BiLSTM S700K switch machine fault diagnosis |
| 基金项目:国家自然科学基金(61763025)项目资助 |
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| 中文摘要: |
| 针对 S700K 转辙机故障诊断有效特征提取困难,信号处理与分类算法难以联合优化的问题,提出了一维卷积神经网络
(1DCNN)与双向长短期记忆神经网络(bidirectional long short-term memory,BiLSTM)结合的转辙机故障诊断方法。 首先,对微机
监测系统采集的转辙机功率曲线进行处理;其次,通过卷积神经网络(convolution neural networks,CNN)的卷积层和池化层对处
理后的数据自适应提取故障特征;再经过扁平层(Flatten)把提取的故障特征作为 BiLSTM 层的输入,进一步挖掘深层次的特征;
最后使用 Softmax 函数实现智能故障诊断。 以某铁路局提供的真实数据验证模型,结果显示所提模型的精确率、召回率和 F1 值
等评价指标分别达到 98. 99%、98. 89%和 98. 89%,相较于其他经典故障诊断模型,1DCNN-BiLSTM 模型在保证训练速度较快的
情况下,将故障诊断的准确率至少提升了 1. 08%。 |
| 英文摘要: |
| Aiming at the problems of S700K switch machine fault diagnosis, which is difficult to extract effective features and signal
processing and classification algorithms, a fault diagnosis method for switch machine combining one-dimensional convolutional neural
network (1DCNN) and bidirectional long short-term memory neural network (BiLSTM) is proposed. Firstly, the power curve of the
switch machine collected by the microcomputer monitoring system is processed. Secondly, the fault features are extracted adaptively from
the processed data by the convolution layer and pool layer of CNN. Then through Flatten, the extracted fault features are taken as the
input of BiLSTM layer to further mine the deep-level features. Finally, the Softmax function is used to implement intelligent fault
diagnosis. The model is validated by the real data provided by a railway bureau. The results show that the accuracy, recall and F1
value
of the proposed model reach 98. 99%, 98. 89% and 98. 89% respectively, which are better than other classical fault diagnosis models,
1DCNN-BiLSTM model improves the accuracy of fault diagnosis by at least 1. 08% when the training speed is fast |
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