【摘要】 针对高精度谐振式露点测量系统中电路故障诊断问题，提出了一种基于改进的麻雀搜索算法（Improved Sparrow Search Algorithm, ISSA）优化智能分类器参数的电路故障诊断模型，采用测前仿真故障诊断方法中的智能诊断方法，选择适用于小样本、非线性问题的支持向量机（Support Vector Machine, SVM）作为智能分类器，针对麻雀搜索算法中收敛速度慢、易陷入局部最优等问题进行改进，并将改进后的优化算法用于SVM参数寻优，构建ISSA-SVM故障诊断模型用于谐振电路故障诊断。实验结果显示，ISSA-SVM模型在建立的电路上能够达到88.9%的故障诊断率，可靠性较强，能够作为高精度谐振式露点传感器电路的故障诊断方法。更多还原

【Abstract】 Aiming at the problem of circuit fault diagnosis in high-precision resonant dewpoint measurement system,this paper proposes a circuit fault diagnosis model based on improved sparrow search algorithm(ISSA) to optimize the parameters of intelligent classifier. The support vector machine(SVM) suitable for small samples and nonlinear problems is selected as the intelligent classifier. In order to solve the problems of slow convergence speed and being easy to fall into local optima of sparrow search algorithm, an improved optimization algorithm is proposed and used to optimize the parameters of SVM, and the ISSA-SVM fault diagnosis model is constructed for resonant circuit fault diagnosis. The experimental results show that the fault diagnosis accuracy rate can reach 88.9 % on the established circuit, and the ISSA-SVM classifier can be used as a high precision resonant dewpoint sensor circuit fault diagnosis method.更多还原