同济大学学报（自然科学版）2024，Vol.52Issue(1)：35-40,6.DOI:10.11908/j.issn.0253-374x.23387
基于主成分分析和深度森林算法的S700K转辙机故障诊断
PCA-gcForest-based Fault Diagnosis of S700K Switch Machine
摘要
Abstract
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.关键词
故障诊断/S700K转辙机/主成分分析(PCA)/深度森林(gcForest)算法Key words
fault diagnosis/S700K switch machine/principal component analysis(PCA)/multi-grain cascade forest(gcForest)algrithm分类
交通工程引用本文复制引用
胡小晨,郭宁,沈拓,董德存..基于主成分分析和深度森林算法的S700K转辙机故障诊断[J].同济大学学报（自然科学版）,2024,52(1):35-40,6.基金项目
国家重点研发计划(2022YFB4300501) （2022YFB4300501）
