Hydraulic pump fault diagnosing method based on LMD-SVD and IG-SVM

Abstract

The invention discloses a hydraulic pump fault diagnosing method based on local mean decomposition (LMD), singular value decomposition (SVD) and information-geometric support vector machine (IG-SVM) so as to improve fault diagnosing accuracy in the case of small samples. The LMD, serving as a self-adaptive signal processing method, can decompose original vibration signals of a hydraulic pump into a limited number of signal components in a self-adaptive mode; then, the signal components are processed through the SVD, the data size of the signal components is compressed, and simpler and more stable fault feature vectors are extracted; finally, the fault states of the hydraulic pump are classified through the IG-SVM, and the fault diagnosing accuracy in the case of the small samples is improved. By the adoption of the fault diagnosing method based on the LMD-SVD-IG-SVM, a complete and effective fault diagnosing scheme in the case of the small samples is provided for the hydraulic pump, and the method has good actual engineering application value.

Description

technical field [0001] The present invention relates to the technical field of hydraulic pump fault diagnosis, in particular to a support vector machine (information-geometric support) based on local mean decomposition (local mean decomposition, LMD), singular value decomposition (singular value decomposition, SVD) and information-geometric support Vector machine, IG-SVM) hydraulic pump fault diagnosis method under small sample conditions. Background technique [0002] The hydraulic pump is a key component of the hydraulic system, which has a very important impact on the stable operation of the entire system. Therefore, an efficient hydraulic pump fault diagnosis method is urgently needed. Since the occurrence of hydraulic pump faults is often accompanied by changes in vibration signals, the diagnostic method based on vibration signals is very important and has become one of the hotspots of related research at home and abroad. However, in practical applications, due to the ...

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Problems solved by technology

However, the performance of SVM depends on the choice of kernel function and its parameter setting, and inappropriate kernel function has a great influence on the classification effect of SVM

However, since people cannot predict the type of data in practical applications, the selection of kernel functions mainly depends on subjective experience, lacking systematic and objective theoretical guidance.

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