Early warning method and system for health state of axle box bearing of train

Abstract

The invention provides a train axle box bearing health state early warning method and system. The method comprises the steps that at least two characteristic parameters of m axle box bearings in the same train are collected in real time, and m is a positive integer; calculating a characteristic index of at least one characteristic parameter of each axle box bearing; respectively calculating a mean value of all feature indexes and / or feature parameters at the current moment; calculating a deviation value of each characteristic index and / or characteristic parameter of each axle box bearing at the current moment according to the mean value; judging whether the deviation value of the characteristic index and / or the characteristic parameter of each axle box bearing exceeds the deviation threshold value of the corresponding characteristic index or the characteristic parameter or not; if a certain axle box bearing exceeds the characteristic index of the deviation threshold value and / or the variety number of the characteristic parameters reaches a preset value, health early warning is sent to the axle box bearing. The method effectively solves the problem that accurate early warning of the train axle box bearing health state is difficult.

Description

technical field [0001] The invention relates to the field of health state monitoring of mechanical components, in particular to a method and system for early warning of the health state of a train axle box bearing. Background technique [0002] Axlebox bearings are the key basic components of the running parts of rail transit trains such as high-speed railways and subways. Once the bearings of the running parts of the train fail, it will cause the train to stop running and reduce its efficiency, and it will also cause major safety issues such as shaft cutting and rollover. ACCIDENT. Therefore, real-time monitoring of the working status and health status of train bearings to realize early warning of bearing failures has important social value to ensure the safety of trains and protect the safety of passengers. [0003] At present, the commonly used method is to calculate the corresponding time domain, frequency domain, time-frequency domain characteristics by monitoring the ...

AI Technical Summary

Problems solved by technology

However, the real-time speed, load and other information of the train are changeable, and it is difficult to measure accurately. The multi-level and multi-dimensional alarm thresholds are mostly determined by expert experience or a small number of samples, resulting in false positives and missed negatives when traditional methods are used to judge the health status of bearings. , misreporting and other issues

In addition, in the traditional fault diagnosis method, the introduction of operating condition information such as speed and load also brings a large amount of necessary real-time calculations, which increases the burden on the bearing health status monitoring system

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