Tumout Fault Diagnosis Based on Improved Shuffled Frog Leaping Algorithm to Optimize SVM Model
In response to the problems of limited fault samples and difficult fault diagnosis caused by the difficulty in simulating turnout faults,this paper presented a support vector machine(SVM)model optimized by improved shuffled frog leaping algorithm(SFLA)to diagnose turnout faults based on small sample data.SVM requires optimal parameter selection to avoid overfitting or underfitting.In this paper,when the integration of differential evolution algorithm,simu-lated annealing algorithm and the SFLA solved the problem of the SFLA easily falling into local optimum,it was used to optimize the parameters of SVM to improve the fault diagnosis ability of the SVM model.The measured data of experi-ment show that under the same conditions,the proposed model improves the average fault diagnosis accuracy by 34.28％compared with the SVM model and the SVM model optimized with SFLA and improves the average fault diagnosis accura-cy by 5.71％,compared with the SVM model optimized by the SFLA that only integrates the differential evolution algo-rithm.The method proposed in this paper based on small sample data is more effective for turnout fault diagnosis.
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