Aiming at the frequent faults of high-speed railway turnout equipment and the heavy workload of on-site main-tenance,a fault diagnosis model of high-speed railway turnouts was proposed based on multiple dimensional scaling(MDS)and support vector machine(SVM)optimized by improved sparrow search algorithm(SSA).Firstly,based on the switching power curve of ZDJ9 turnout and the summary of the fault types and causes of typical turnouts in the field,the time-domain and frequency-domain characteristic indexes of the turnout power curve and the wavelet packet energy entropy were extracted respectively to form the characteristic index vector.Secondly,by using the MDS method to opti-mize the dimension reduction of multidimensional feature indicators,the sample database of fault diagnosis feature indi-cators was established.Finally,the improved Circle chaotic map was used to initialize the population,before the diversi-ty of sparrow population was enhanced through the adaptive t distribution.By optimizing the two key parameters of penal-ty factor and kernel function variance in the SVM model using the improved SSA algorithm,the improved SSA-SVM turnout fault diagnosis model was constructed to realize the turnout fault diagnosis.The fault diagnosis results show that this model,with the fault diagnosis accuracy as high as 96.25％,demonstrates better diagnostic effect than other meth-ods,which can provide a theoretical basis for the fault maintenance of turnout equipment.
维普
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