ANALOGUE CIRCUIT FAULT DIAGNOSIS METHOD BASED ON GENERALIZED MULTIPLE KERNEL LEARNING-SUPPORT VECTOR MACHINE

WIPO Patent Application WO/2017/128455

A1

An analogue circuit fault diagnosis method based on a generalized multiple kernel learning-support vector machine (GMKL-SVM), comprising the following steps: (1) collecting a time domain response signal of an analogue circuit, i.e. collecting an output voltage signal of the analogue circuit; (2) performing wavelet transform of the collected voltage signal, calculating the energy of a wavelet coefficient as a characteristic parameter, the set of all the characteristic parameters being sample data; (3) applying, based on the sample data, PSO to optimize a regularization parameter and a compromise parameter of the generalized multiple kernel learning-support vector machine, and constructing a GMKL-SVM-based fault diagnosis model; and (4) using the constructed GMKL-SVM-based fault diagnosis model as a classifier to diagnose a fault of the analogue circuit. The classification property of the GMKL-SVM in the present invention is better than those of other classification algorithms, and the method of applying PSO to optimize the GMKL-SVM parameters is also better than the conventional parameter acquisition methods, being capable of effectively detecting an element fault of the analogue circuit.

HE YIGANG (CN)
ZHANG CHAOLONG (CN)
LI ZHIGANG (CN)
ZUO LEI (CN)

PCT/CN2016/073449

August 03, 2017

February 04, 2016

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UNIV HEFEI TECHNOLOGY (CN)

G01R31/316; G06K9/62

CN105548862A	2016-05-04
CN104198924A	2014-12-10
CN101587155A	2009-11-25
CN101221213A	2008-07-16
KR292150B

CHANGSHA XINGYAO PATENT FIRM (CN)

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