Fault diagnosis using Support Vector Machines(SVM)with improved kernel functions in natural gas desulfurization system
To address the issue of slow response and low diagnostic accuracy inherent in traditional desulfurization system fault diagnosis methods,this paper proposes a Support Vector Machine(SVM)fault diagnosis method for natural gas desulfurization system based on improved kernel function.According to Mercer's theory,this method redesigns the SVM's kernel function and its parameters,by integrating polynomial,Sigmoid,and radial basis kernel functions into a single composite kernel.Compared with the traditional SVM method,this improved approach not only combines the benefits of each single kernel function but also offers higher learning efficiency and diagnostic accuracy,maintaining strong generalization capabilities even with limited sample data.Experimental validation conducted using HYSYS modeling and field data demonstrates that the improved method reduces error rate to approximately 30%of the its original method before improvement,thereby verifying the method's effectiveness in improving the accuracy and efficiency of the fault diagnosis of desulfurization systems.The research results contribute to the intelligent operation of fault diagnosis systems in natural gas desulfurization systems and also provide a reference for the study of fault diagnosis methods.
Improved kernel functionsSupport Vector Machines(SVM)HYSYSNatural gas desulfurizationFault diagnosis
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