Research on Switch Machine Fault Diagnosis Based on Integrated Deep Learning

Abstract

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.

Keywords

• urban rail transit
• turnout
• switch machine
• fault diagnosis