Rotating machinery fault diagnosis based on fuzzy proximal support vector machine optimized by particle swarm optimization

Based on particle swarm optimization(PSO) and fuzzy linear proximal support vector machine(FLPSVM),a rotating machinery fault diagnosis method was proposed.Fuzzy PSVM can solve the question that standard PSVM is sensitive to outliers and noises in training sets.When PSVM is applied to the problem with two classes on unbalanced datasets,it tends to fit better the class with more data points.This leads to the poor classification performance.Different penalty factors were designed for different samples in order to improve the classification performance.The penalty factors of FLPSVM were optimized by the canonical particle swarm optimization to avoid the dependence on initial parameters and training samples.The rotating machinery fault-classification data were used to demonstrate the designed method.The vibration signals were filtered by the filters at first and then the energy at spetral peaks of different frequencies was taken as the input feature parameters of FLPSVM classifier to identify the five typical rotating machinery faults.The experiment results demonstrate that the modeling method is correct and precise.