文章基本信息

标题：Fault Diagnosis of the Planetary Gearbox Based on ssDAG-SVM ⁎
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作者：Cui Lihui ; Liu Yang ; Zhou Donghua 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：24
页码：263-267
DOI：10.1016/j.ifacol.2018.09.586
语种：English
出版社：Elsevier
摘要：AbstractPlanetary gearbox is of great significance for many practical cases, and many data-driven approaches have been employed to solve the fault diagnosis problem for the system. Among these methods, Directed Acyclic Graph Support Vector Machines (DAG-SVM) has been widely adopted due to its ability to handle the multi-class problem. Different from traditional DAG-SVM, a structure-selected DAG-SVM (ssDAG-SVM) is proposed such that the diagnosis performance will not degrade because of inappropriate node structure. By introducing the concept of class separability, the principle of evaluating the degree of class separability is integrated into the process of constructing the DAG-SVM structure. Subsequently, a proper structure can be selected to realize the planetary gearbox fault diagnosis with high accuracy. Finally, the effectiveness of the method is illustrated by some practical experiments.
关键词：KeywordsPlanetary gearboxfault diagnosisDAG-SVMclass separability