基于主成分分析和深度森林算法的S700K转辙机故障诊断OACSTPCD

To overcome the shortage of the existing fault diagnosis methods such as low accuracy and efficiency,a fault diagnosis method based on principal component analysis(PCA)and multi-grain cascade forest(gcForest)algorithm was proposed.PCA was used to simplify the current eigenvalue for 11 fault modes of S700K switch machine.And an improved gcForest model with the simpler eigenvalue embedded was used to improve the data processing capability and enhance the inherent feature representativeness of the model.The experimental results show that the fault diagnosis accuracy of the improved gcForest model is 97.62%,which verifies the effectiveness and superiority of the method.