基于1DCNN-BiLSTM组合模型的S700K转辙机故障诊断

Fault diagnosis of S700K switch machine based on 1DCNN-BiLSTM hybrid model

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%。

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 F1value 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.