噪声与振动控制2024，Vol.44Issue(3)：125-131,7.DOI:10.3969/j.issn.1006-1355.2024.03.019
全矢融合的二元PELCD样本熵列车故障诊断
Train Fault Diagnosis Based on Binary PELCD Sample Entropy with Full Vector Fusion
摘要
Abstract
The service state of high-speed train for a long time operation will cause the deterioration of the performance of the key components of its bogie,and the breakdown of the safety events will cause serious economic losses and even casualties.In this paper,considering the characteristics of high-speed train vibration signals,the partial integrated local feature scale decomposition method is extended to the field of binary signal processing.At the same time,based on the theory of full vector spectrum,the information fusion of the same order component signals is carried out to obtain more complete data features,and the sample entropy features of the fused data are extracted to obtain the train fault features.The Grey Wolf optimization algorithm is used to optimize the parameters of support vector machine.Finally,the fault recognition rates under single fault condition,compound fault condition and component performance degradation are compared by experiments to verify the effectiveness and superiority of the proposed method.关键词
故障诊断/二元部分集成的局部特征尺度分解方法/全矢理论/灰狼优化算法/支持向量机Key words
fault diagnosis/binary partial ensemble local characteristic scale decomposition/full vector theory/grey wolf optimization algorithm/support vector machine分类
信息技术与安全科学引用本文复制引用
郑航,李刚,李德仓..全矢融合的二元PELCD样本熵列车故障诊断[J].噪声与振动控制,2024,44(3):125-131,7.基金项目
国家自然科学基金资助项目(62063013) （62063013）
