基于离散粒子群和支持向量机的故障诊断方法

针对与故障不相关的变量会影响分类器性能，从而导致故障诊断正确率下降，提出一种将离散粒子群算法（PSO）与支持向量机（SVM）相结合寻找故障特征变量的优化算法。该算法实现了数据降维和故障特征保留，有效地提高了故障诊断性能。基于连续搅拌釜式反应器（CSTR）的仿真实例验证了该算法古白有诗性．

Fault Diagnosis Based on Discrete Particle Swarm Optimization and Support Vector Machine

To overcome the disadvantages that irrelevant variables spoil classifiers and decrease the correct classification rates of faults,a new optimization algorithm based on discrete particle swarm optimization(PSO) and support vector machines(SVM) is presented to directly search for fault feature variables.As it can reduce the dimensionality of data space and preserve the fault features,the algorithm greatly improve the performance of fault diagnosis.The simulation results of fault diagnosis on continuous stired-tank reactor(CSTR) prove its validity.