Electric locomotive idling online identification method based on fuzzy entropy and kernel extreme learning machine

Abstract

The invention discloses an electric locomotive idling online identification method based on fuzzy entropy and a kernel extreme learning machine. The method comprises a multi-scale fuzzy entropy feature extraction module, an optimal kernel extreme learning machine model module and an idling online identification module. Compared with the prior art, the method has the positive effects that idling is identified from the angles of signal feature extraction and machine learning classification, features capable of remarkably representing the idling state / adhesion state are extracted from accurately measured locomotive wheelset speed signal features, and the features are classified by a classifier to realize idling online identification; compared with a traditional idling recognition method adopting input with errors, the online recognition effect is more excellent, more accurate and quicker; compared with a traditional method, the method is higher in adaptability to complex operation conditions and operation environments of the electric locomotive, has more excellent recognition precision compared with a traditional idling recognition method, and effectively solves the problem that a threshold value is difficult to set during online recognition in the traditional method.

Description

technical field [0001] The invention relates to rail transit train traction control technology, in particular to an online identification method for idling electric locomotives. Background technique [0002] Transportation plays an increasingly important role in economic construction, and railway transportation is the backbone of the transportation field. In railway transportation, the adhesion between the moving wheels and the wheel rails of electric locomotives is the ultimate driving force to drive the locomotives. Therefore, only by ensuring that the effective adhesion between the wheel rails is not destroyed, that is, to avoid idling of the locomotive wheels, can the traction be effectively used. The output power of the motor maximizes the power utilization of the traction motor. However, due to the operating conditions of the locomotive and the complex environment, and the adhesion between the wheel and rail is largely dependent on the condition of the wheel-rail cont...

AI Technical Summary

Problems solved by technology

[0021] From the foregoing, it can be seen that the current main locomotive idling identification methods are all signal analysis methods directly based on input data, and there is no effective extraction of high-precision features representing the idling state in the signal for idling identification; at the same time, the existing methods are accurate in online identification. There are deficiencies in performance, rapidity, linearity or anti-interference

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