基于多标签学习的旋转机械分级复合故障诊断
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作者单位:

1.北京化工大学;2.山东科技大学

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TP273

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国家自然科学基金项目(面上项目,重点项目,重大项目)


Hierarchical compound fault diagnosis of rotating machinery based on multi-label learning
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1.Beijing University of Chemical Technology;2.Shandong University of Science and Technology

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    摘要:

    传统故障诊断方法多是针对单一故障类型, 然而实际工业中多种故障会同时出现, 即复合故障. 针对复合故障诊断问题, 一些学者引入了多标签学习思想, 多标签K 近邻算法(ML-KNN)就是其中之一. 然而ML-KNN算法作为一阶算法, 忽略了标签间的联系. 本文提出了一种分级多标签学习算法, 名为分层多标签K近邻算法(HML-KNN). HML-KNN算法将机械的退化阶段和故障类型分为两级, 将第一级得到的标签信息进行转化, 转化后的信息作为新特征放入第二级进行判断.HML-KNN算法是一种高阶算法, 考虑了全局的标签信息. 通过在XJTU-SY数据集上的验证, 体现了HML-KNN 算法在处理复合故障诊断问题上的优越性.

    Abstract:

    Traditional fault diagnosis methods are mostly for a single fault type at one time, but in the actual industry, many kinds of faults will occur at the same time, that is, compound fault. For the problem of compound fault diagnosis, some scholars have introduced the method of multi-label learning, and multi-label K-nearest neighbor (ML-KNN) algorithm is one of them. However, as a first-order algorithm, ML-KNN ignores the relationship between labels. In this study, a hierarchical multi-label learning algorithm is proposed, named hierachical multi-label K-nearest neighbor (HML-KNN). HML-KNN algorithm categorizes the degradation state as the first level and fault type of machinery as the second level. The first level label information is transformed, and the transformed information is put into the second level as new features for judgment. HML-KNN algorithm is a high-order algorithm, which considers the global label information. Through the verification on XJTU-SY bearing data set, the superiority of HML-KNN algorithm in dealing with composite fault diagnosis is demonstrated.

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  • 收稿日期:2021-01-12
  • 最后修改日期:2021-04-07
  • 录用日期:2021-04-21
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