粒子群优化混合核支持向量机的换向阀与液压缸故障诊断方法OA

To address the complex fault characteristics and low fault diagnosis accuracy of directional-control valves and hydraulic cylinders in hydraulic systems,a hybrid kernel support vector machine algorithm that combines a linear kernel and a Gaussian kernel is proposed.This algorithm can significantly enhance the classification capability of support vector machine for complex fault data.On this basis,the hybrid kernel support vector machine algorithm combines particle swarm optimization with a one-vs-one multi-classification strategy,enabling the hybrid kernel support vector machine algorithm to perform parameter optimization and multi-classification.To validate the algorithm's effectiveness,a fault experimental setup for directional-control valves and hydraulic cylinders is established to collect fault flow signal data.The data is then preprocessed through time-domain feature extraction and principal component analysis.Subsequently,the preprocessed data is input into the improved fault diagnosis model for training and validation.The classification results show that this method achieves an accuracy of 97.11%in the fault diagnosis of directional-control valves and hydraulic cylinders.Compared with other fault diagnosis models,this model demonstrates higher fault diagnosis accuracy and superior classification performance.