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OA北大核心CSTPCD
Multi-domain Feature Extraction Combined with AdaBoost for Fault Diagnosis of Speed-up Turnout with Unknown Fault
针对提速道岔未知新故障误判影响列车安全运行及道岔检修效率的问题,提出一种基于多域特征提取和自适应提升算法(Adaptive boosting,adaboost)的信号分析及故障诊断模型.首先,为了深入挖掘道岔的故障特征,分别从时域、频域及时频域提取故障特征,构造原始特征集;然后根据AdaBoost模型获得的特征重要度排序构造不同特征数量的分类模型,并利用模型分类精度进一步获得最佳特征子集;最后将最佳特征子集输入含判定机制的AdaBoost故障诊断…查看全部>>
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 t…查看全部>>
郑云水;张亚宁
兰州交通大学自动化与电气工程学院,兰州 730070兰州交通大学自动化与电气工程学院,兰州 730070
交通运输
特征提取adaboost未知故障提速道岔故障诊断
feature extractionadaboostunknown faultspeed-increasing turnoutfault diagnosis
《机械科学与技术》 2024 (8)
1350-1358,9
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)
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