Research on Switch Machine Fault Diagnosis Based on Integrated Deep Learning
Journal Title: Urban Mass Transit - Year 2024, Vol 27, Issue 4
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.
Authors and Affiliations
Xuezhi LI, Yonghao YANG, Xulei WANG, Zhilong OU, Jinlong PI
Keywords
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- EP ID EP734195
- DOI 10.16037/j.1007-869x.2024.04.049
- Views 57
- Downloads 0
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