An Adaptive Doppler Correction Method Using Spectrum Kurtosis as Optimal Index

Abstract

The invention discloses an adaptive Doppler correction method using spectrum kurtosis as an optimization index, which can be applied to the field of acoustic detection of high-speed train bearing rails. Optimize the index and automatically identify the following parameters required for Doppler correction through optimization calculation: train running speed v, vertical vertical distance r between the microphone and the straight line where the sound source motion track of the bearing is located, and the distance between the sound source of the bearing and the microphone at the zero moment of the signal The lateral distance x, based on the above automatically identified parameters, can realize the adaptive Doppler distortion correction of the trackside signal. The self-adaptive Doppler correction method proposed by the present invention does not require any external transducers except the microphone, and compared with the traditional parameter extraction method based on time-frequency analysis, it has more advantages in anti-noise ability, and at the same time, the calculation efficiency is also improved. With the improvement, it can be used for acoustic on-line detection of rail edge of high-speed train bearings.

Description

technical field [0001] The present invention is applied to the technical field of acoustic fault detection on the rail edge of a high-speed train wheel set bearing, and specifically relates to an adaptive Doppler correction method using spectral kurtosis as an optimization index, which can be based on signal without relying on any other sensors except microphones The trackside parameters of the high-speed train are extracted by itself for Doppler distortion correction. Background technique [0002] The sound signal sent by the wheel-set bearing when the train is running at high speed contains rich operating status information of the bearing. Installing a microphone on the side of the track to collect the sound signal of the bearing and using signal processing methods can effectively detect the health and fault diagnosis of the wheel-set bearing. However, when the train is running at high speed, Doppler distortion will occur in the collected trackside acoustic signals, which ...

AI Technical Summary

Problems solved by technology

However, when the train is running at high speed, Doppler distortion will occur in the collected trackside acoustic signals, which brings difficulties to the signal processing and reduces the accuracy of the diagnosis results to a certain extent. A feasible method is to The distorted signal is resampled and corrected, but the premise is that the parameters of the rail edge need to be obtained. The existing method is to use the speed sensor and the distance sensor to obtain the parameters. However, with the development of technology, it does not depend on the sensor, but obtains the parameters based on the signal itself. , so as to achieve adaptive correction. One of the existing adaptive correction methods is based on time-frequency analysis, and the other is based on Doppler-let transform. However, these two methods have a large amount of calculation, and the anti-noise ability needs to be improved.

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