机械科学与技术2024，Vol.43Issue(8)：1350-1358,9.DOI:10.13433/j.cnki.1003-8728.20230043
多域特征提取结合AdaBoost的含未知故障提速道岔故障诊断方法
Multi-domain Feature Extraction Combined with AdaBoost for Fault Diagnosis of Speed-up Turnout with Unknown Fault
摘要
Abstract
In view of the problem that the unknown new faults of speed-increasing turnouts are misjudged,which will affect the operation safety of trains and the turnouts maintenance efficiency.A signal analysis and fault diagnosis model based on multi-domain feature extraction and adaptive boosting algorithm(AdaBoost)is proposed.Firstly,in order to deeply extract the fault features of turnouts,the original feature set is constructed by extracting fault features from time domain,frequency domain,and time-frequency domain respectively.Secondly,the classification models with different numbers of features are constructed based on the feature importance ranking obtained by the AdaBoost model,and the best feature subset is further obtained by using the classification accuracy of the model.Finally,the best feature subset is input into the AdaBoost fault diagnosis model with a decision mechanism to complete the diagnosis of unknown faults on the speed-increasing turnout.Meanwhile,through the retraining of the model,the adaptive update of the existing fault diagnosis model is realized.Research indicates:the algorithm in this paper can effectively extract the fault features and improve the diagnosis accuracy of the known faults of the turnout,at the same time,the method can effectively identify new faults that have not occurred ago.关键词
特征提取/adaboost/未知故障/提速道岔/故障诊断Key words
feature extraction/adaboost/unknown fault/speed-increasing turnout/fault diagnosis分类
交通工程引用本文复制引用
郑云水,张亚宁..多域特征提取结合AdaBoost的含未知故障提速道岔故障诊断方法[J].机械科学与技术,2024,43(8):1350-1358,9.基金项目
国家自然科学基金地区科学基金项目(61763023)与甘肃省科技厅计划(20YF8GA037) （61763023）
