Research on Switch Machine Fault Prediction Based on CHMM

Abstract

Objective As an important component of urban rail transit signal equipment, once the switch machine malfunctions, the operation will be seriously affected. Monitoring and predicting its health status is particularly important. Method A fault prediction method for switch machine based on CHMM (continuous hidden Markov model) is proposed. The features of the switch machine degradation state are extracted, and the original input data dimension is reduced based on t-SNE algorithm to reduce the redundant features. Spectral clustering algorithm is used to determine the optimal number of degradation states, make clustering segmentation and analyze the degradation state features of the switch machine action power curve. Based on CHMM model and fault diagnosis model, the switch machine fault prediction is realized by constructing degradation state identification model and fault identification model. The fault prediction method for switch machine based on CHMM is verified through measured data. Result & Conclusion With the normal operation power curve of the switch machine as the research object, the above method taps the monitored data deeply, and the extracted degradation state features have good expressive ability. According to the matching results between the curve model in severely degraded state and the normal curve model, the fault types of switch machine can be predicted when the power of the switch machine fluctuates abnormally.

Keywords

• urban rail transit
• signal system
• switch machine
• fault prediction
• degradation state identification
• chmm