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Pattern recognition of a sensitive feature set based on the orthogonal neighborhood preserving embedding and adaboost_SVM algorithm for rolling bearing early fault diagnosis
Measurement Science and Technology ( IF 2.4 ) Pub Date : 2020-07-19 , DOI: 10.1088/1361-6501/ab8c11
Fafa Chen 1, 2 , Mengteng Cheng 1 , Baoping Tang 2 , Baojia Chen 1 , Wenrong Xiao 1
Affiliation  

Early fault diagnosis is a hotspot and difficulty in the research of mechanical fault diagnosis. An early fault diagnosis method based on the orthogonal neighborhood preserving embedding and Adaboost_SVM algorithm for rolling bearing early fault diagnosis is proposed in this paper. Firstly, the vibration signals of rolling bearings are measured online. The correlation coefficients between the early fault indicators and the performance degradation are deeply analyzed based on the full-lifetime vibration data of rolling bearings so as to select the sensitive fault indicators for further fault diagnosis. Secondly, the orthogonal neighborhood preservation embedding (ONPE) is employed to eliminate the redundant information from the original multi-domain feature set. Finally, the classical SVM is improved to form the Adboost-SVM for the early fault diagnosis of rolling bearings. The feasibility and validity of this method are verified by applying the early fault diagnosis of rolling b...

中文翻译:

基于正交邻域保留嵌入和adaboost_SVM算法的滚动轴承早期故障诊断敏感特征集的模式识别

早期故障诊断是机械故障诊断研究的热点和难点。提出了一种基于正交邻域保留嵌入和Adaboost_SVM算法的滚动轴承早期故障诊断方法。首先,在线测量滚动轴承的振动信号。基于滚动轴承的全寿命振动数据,对早期故障指标与性能下降之间的相关系数进行了深入分析,以选择敏感的故障指标进行进一步的故障诊断。其次,采用正交邻域保留嵌入(ONPE)从原始多域特征集中消除冗余信息。最后,对经典支持向量机进行了改进,形成了Adboost-SVM,可用于滚动轴承的早期故障诊断。通过对滚动轴承进行早期故障诊断,验证了该方法的可行性和有效性。
更新日期:2020-07-20
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