Fault classification method based on improved fuzzy support vector machine

Abstract

The invention discloses a fault classification method based on an improved fuzzy support vector machine. The method comprises the steps that sample data of a switch cabinet are acquired, an optimization model is established for the sample data by introducing a relaxation factor, and the minimum radius of the hyper-sphere of the optimization module is calculated; the membership degree of the sample data inside and outside the boundary of the hyper-sphere is calculated by using a segmented half-time normal cloud model with the minimum radius of the hyper-sphere acting as the boundary of the hyper-sphere; the sample data of the switch cabinet, fault classification marks and the membership degree form a sample set of the fault types, and the optimal segmented function of the improved FSVM is acquired by using the sample set of the fault types; and the classifier of the improved FSVM is established by using the optimal segmented function of the improved FSVM, and the sample data pass through the classifier of the improved FSVM in turn so that the fault type of each sample data is acquired. The effect of fault classification of the switch cabinet can be enhanced by the method.

Description

technical field [0001] The invention relates to the technical field of fault classification, in particular to a fault classification method based on an improved fuzzy support vector machine. Background technique [0002] At present, the operation status of cables, busbars and circuit breakers is monitored, fault characteristic parameters are extracted, and typical fault classification and identification can be realized by using fault characteristic data, which is conducive to the identification and diagnosis of various fault states and guides its operation and maintenance management. For the classification and identification of faults, there are currently decision tree classification, Bayesian classification, neural network classification and support vector machine classification, among which support vector machine (Support Vector Machine, SVM) can obtain training learning accuracy and test accuracy on small sample data. The optimal compromise between recognition capabilitie...

AI Technical Summary

Problems solved by technology

For the classification and identification of faults, there are currently decision tree classification, Bayesian classification, neural network classification and support vector machine classification, among which support vector machine (Support Vector Machine, SVM) can obtain training learning accuracy and test accuracy on small sample data. The optimal compromise between recognition capabilities has good adaptability, but SVM treats all e

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