Adaptive Doppler correction method with spectral kurtosis as optimization index

Abstract

The present invention discloses an adaptive Doppler correction method with spectral kurtosis as an optimization index, which can be applied to the field of high-speed train bearing trackside acousticdetection. Specifically, a spectral kurtosis index of a resampled signal is used as an optimization index, and the following parameters required for Doppler correction are automatically identified byoptimization calculation: train running speed v, longitudinal vertical distance r between a microphone and a straight line where a moving track of a bearing sound source is located, and transverse distance x between the bearing sound source and the microphone at zero time of signal. The trackside signal adaptive Doppler distortion correction can be realized on the basis of the above automaticallyidentified parameters. The adaptive Doppler correction method provided by the present invention does not require any external transducer except for the microphone, and has an advantage in the anti-noise capability compared with the conventional parameter extraction method based on time-frequency analysis. Furthermore, the calculation efficiency has also been improved, and the method can be used for high-speed train bearing trackside acoustic on-line detection.

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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