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

Abstract

The invention discloses a high-speed train bearing temperature anomaly detection method based on a space-time fusion decision. A time dimension bearing temperature anomaly detection model and a space dimension bearing temperature anomaly detection model are fused, and a D-S evidence theory is combined to detect a bearing temperature anomaly condition based on time-space fusion. According to the method, the bearing temperature anomaly detection models in the two dimensions of time and space are combined by using the D-S evidence theory, so that the false alarm rate can be reduced, a relatively large early warning amount can be ensured, and the effects of enhancing the advantages and avoiding the disadvantages of the time and space dimension models are realized.

Description

technical field [0001] The invention relates to a method for detecting abnormal bearing temperature of a high-speed train, which belongs to the field of abnormal bearing temperature prediction during high-speed train operation, and is a method for detecting abnormal bearing temperature during high-speed train operation based on deep learning. Background technique [0002] The high-speed train bearing temperature prediction method is to transmit the original signal 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 signal to establish a mapping relationship with the real-time prediction of the bearing temperature, and finally The intelligent bearing temperature prediction algorithm is used to predict the bearing temperature for a period of time after the current time, and use it as the basis for the judgment of the abnormal state of the bearing. [0003] The current vehicle-mounted...

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Problems solved by technology

[0003] The current vehicle-mounted intelligent operation and maintenance system for high-speed trains is mainly a monitoring of bearing temperature, and the identification of bearing status 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 bearings and vehicle accidents occur, so early abnormal detection of bearing 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 abnormal temperature of high-speed train bearings, but there are still defects that it is difficult to realize the early warning of abnormal temperature rise of the vehicle-mounted intelligent operation and maintenance system.

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