High-speed train temperature anomaly detection method based on space-time fusion decision

Abstract

The invention discloses a high-speed train temperature anomaly detection method based on space-time fusion decision. The time-dimension axle temperature anomaly detection model and the space-dimensionaxle temperature anomaly detection model are fused, and the temperature anomaly condition of the high-speed train part is detected based on space-time fusion in combination with the DS evidence theory. According to the method, the temperature anomaly detection models of two dimensions of time and space are combined by utilizing the DS evidence theory, so that the false alarm rate can be reduced,a relatively large early warning amount can be ensured, and advantages and disadvantages of time and space dimension models are improved.

Description

technical field [0001] The invention relates to a method for detecting abnormal temperature of a high-speed train, which belongs to the field of abnormal temperature prediction during operation of a high-speed train, and is a method for detecting abnormal temperature during operation of a high-speed train based on deep learning. Background technique [0002] The high-speed train temperature prediction method is to transmit the original signals collected by several sensors to the computer during the running of the high-speed train, and perform a series of signal processing on the original signals to establish a mapping relationship with real-time temperature prediction, and finally through intelligent The temperature prediction algorithm is used to predict the temperature of vehicle components for a period of time after the current time, and use it as the basis for abnormal state discrimination. [0003] The current vehicle-mounted intelligent operation and maintenance system...

AI Technical Summary

Problems solved by technology

[0003] The current vehicle-mounted intelligent operation and maintenance system for high-speed trains is mainly a monitoring of temperature, and the state identification of vehicle components is realized by formulating corresponding logic rules and temperature thresholds. This method can avoid major safety accidents, but once such an alarm occurs, it means Significant damage to components, accidents in vehicle operation, and early abnormal detection of component temperature, fault location and early warning cannot be achieved

[0004] In recent years, the use of temperature data has provided a wealth of ideas and methods for the detection of temperature anomalies in high-speed trains, but there are still defects that it is difficult to realize the early warning of abnormal temperature rises in the on-board intelligent operation and maintenance system.

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