A method and device for feature extraction of abrasive particle signal based on logarithm-kurtosis

Abstract

The present invention claims to protect a method and device for feature extraction of abrasive particle signals based on log-kurtosis. The method is mainly divided into three parts: experimental data segmentation, calculation and optimization of kurtosis value, and linearization of kurtosis index. First, the experimental data is divided into M sections according to the acquisition time, and the experimental data of each section is equal; then, the kurtosis value of the M section of experimental data is calculated, and the K-means algorithm is used to classify the kurtosis value to remove electromagnetic interference. and abnormal kurtosis values ​​caused by uneven distribution of abrasive grains; finally, calculate the mean and mean square error of the kurtosis data, and perform logarithmic processing to obtain the log-kurtosis parameter index. After experimental verification, the log-kurtosis parameter index extracted by the algorithm has a high linearity with the actual abrasive particle concentration in the oil, reaching 0.99, which can effectively measure the abrasive particle concentration in the oil.

Description

technical field [0001] The invention belongs to the technical field of abrasive particle monitoring, and in particular relates to a feature extraction method and device for an online oil abrasive particle signal. Background technique [0002] Wear is one of the main factors affecting the reliability and service life of mechanical equipment. Metal abrasive particles generated during the operation of mechanical equipment will remain in the lubricating oil and circulate with the lubricating oil circuit. Since these abrasive particles carry a large amount of effective information about the wear intensity and wear mode of mechanical equipment, the wear form and wear state of mechanical equipment can be indirectly detected by extracting characteristic information such as the concentration of abrasive particles, the material, size and shape of the abrasive particles. The main cause of mechanical equipment failure is the failure of parts, and the wear effect of parts is the most co...

AI Technical Summary

Problems solved by technology

The traditional method of counting wear particles is to go through all the sampling points and count all the wear particle signals, but the traversal algorithm needs to compare each point, which takes a long time

Secondly, the real wear particle signal is more complicated, and the traditional counting method will cause errors

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