Electric locomotive idling identification method based on empirical wavelet Hilbert transform

Abstract

The invention discloses an electric locomotive idling identification method based on empirical wavelet Hilbert transform. The method comprises steps of performing empirical wavelet transform and Hilbert transform on the locomotive wheelset speed through an idling trend recognition system to obtain a Hilbert energy spectrum, and further obtaining a current idling trend value of the locomotive through idling trend recognition based on a time frequency-energy spectrum; meanwhile, obtaining a current working condition information value through a traction moment instruction; and finally, comprehensively judging the idling state of the locomotive by combining the current idling trend value, the current working condition information value and a differential idling judgment value judged by a differential threshold method, and identifying the idling state of the locomotive. Compared with the prior art, the method has the advantages that the time-frequency information of the input signal is comprehensively utilized, and idling recognition is more accurate and faster; the adaptability to complex operating conditions and operating environments of electric locomotives is higher; the value requirements on threshold values such as gain coefficients are not strict; interference of noise signals can be effectively filtered out, and key signals containing idling are extracted; interference of train vibration on a recognition algorithm is effectively avoided, and idling recognition precision is improved.

Description

technical field [0001] The invention relates to an electric locomotive idling identification method based on empirical wavelet Hilbert transform. Background technique [0002] In railway transportation, the adhesion between the moving wheels and the wheel rails of electric locomotives is the ultimate driving force for driving the locomotives. Therefore, only by ensuring that the effective adhesion between the wheels and rails is not destroyed, that is, avoiding idling of the locomotive wheels, can effective use of traction be achieved. The output power of the motor maximizes the power utilization of the traction motor. However, the adhesion performance between the wheel and the rail is affected by many factors, such as leaves, grease, ice, snow, water, etc., and these factors will cause the adhesion between the wheel and the rail to decline sharply. When the adhesion condition between the wheels and rails of the locomotive becomes poor or damaged, the wheels of the electric...

AI Technical Summary

Problems solved by technology

[0010] In summary, the existing locomotive idling identification methods are all based on time-domain signal analysis methods, which do not effectively use the frequency domain information of the signal. At the same time, the existing methods have defects in identification accuracy, rapidity or anti-interference.

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