A bearing temperature prediction and alarm diagnosis method based on lstm model

Abstract

The invention discloses a bearing temperature prediction and alarm diagnosis method based on the LSTM model; firstly, a bearing temperature prediction model is generated, then the model input data is obtained according to the real-time monitoring temperature, and then the target axial temperature data is predicted; finally, the bearing is judged according to the temperature alarm mechanism Running state, output alarm result. The invention predicts the temperature of the target shaft position through the temperature value of the auxiliary shaft position, adopts the long-short-term memory network model, and avoids the temperature jump and temperature constant interference of the bearing fault alarm system based on the temperature sensor during use. False positives and false negatives occur, which can analyze the correlation relationship in time series and predict the trend of time series data. At the same time, it also overcomes the shortcomings that other neural networks cannot predict for a long time. The temperature alarm can be performed more accurately.

Description

technical field [0001] The invention relates to the technical field of railway transportation safety monitoring and train fault detection, in particular to a research on temperature prediction and temperature alarm mechanism of bogie rolling bearings based on LSTM recursive neural network model. Background technique [0002] With the rapid development of my country's high-speed, heavy-duty railways and urban rail transit, it poses challenges to the operation and maintenance of rail transit vehicles, and rolling bearings are one of the key components of train bogies, and their operating status directly affects the stability and safety of trains Therefore, breaking through the health status monitoring and online fault diagnosis technology of key components of high-speed trains is an important way to ensure the safe and efficient operation of high-speed trains. In the bogie rolling bearing fault monitoring system developed by several companies, temperature sensors are used to co...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is: false alarms caused by sensor damage, electromagnetic interference, etc. in the existing bogie fault diagnosis system, and temperature alarm problems caused by reducing normal shaft temperature rises such as locomotive high-power operation, etc., bearing alarm system Accuracy and stability are not enough

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