Support vector machines for detection and characterization of rolling element bearing faults
Support vector machines for detection and characterization of rolling element bearing faults
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DOI：
10.1243/0954406011524423
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发表时间：
2001-01-01
影响因子：
2
通讯作者：
Nandi, AK
中科院分区：
文献类型：
--
作者：
Jack, LB;Nandi, AK
人工神经网络（ANN）多年来一直用于检测旋转机械的故障，使用振动信号的统计估计作为输入特征，并且它们已被证明在此类应用中非常成功。支持向量机 (SVM) 是一项较新的发展，在状态监测 (CM) 领域很少使用它们。有限数量的训练数据的可用性给 SVM 的使用带来了一些问题，并且提供了一种策略，可以在只有有限的训练数据可用的情况下显着提高泛化性能。本文研究了两种类型的分类器在给定场景（多类故障表征示例）中的性能。
Artificial neural networks (ANNs) have been used to detect faults in rotating machinery for a number of years, using statistical estimates of the vibration signal as input features, and they have been shown to be highly successful in this type of application. Support vector machines (SVMs) are a more recent development, and little use has been made of them in the condition monitoring (CM) arena. The availability of a limited amount of training data creates some problems for the use of SVMs, and a strategy is offered that improves the generalization performance significantly in cases where only limited training data are available. This paper examines the performance of both types of classifier in one given scenario-a multiclass fault characterization example.