Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms
Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms
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DOI：
10.1006/mssp.2001.1454
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发表时间：
2002-03-01
影响因子：
8.4
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）已被用于旋转机械故障检测多年，使用统计方法预处理的振动信号作为输入功能。人工神经网络已被证明是非常成功的，在这种类型的应用;相比之下，支持向量机（SVM）是一个更新的发展，并很少使用它们在状态监测竞技场。有限数量的训练数据的可用性创建使用支持向量机的某些问题，并提出了一种策略，以提高泛化性能的情况下，只有有限的训练数据可用。本文探讨了两种类型的分类器在两类故障/无故障识别的例子，并试图通过使用基于遗传算法的特征选择过程，以提高这两种技术的整体泛化性能的性能。(C)2002年由Elsevier Science Ltd.出版
Artificial neural networks (ANNs) have been used to detect faults in rotating machinery for a number of years, using statistical methods to preprocess the vibration signals as input features. ANNs have been shown to be highly successful in this type of application; in comparison, support vector machines (SVMs) are a more recent development, and little use has been made of them in the condition monitoring arena. The availability of a limited amount of training data creates certain problems for the use of SVMs, and a strategy is advanced to improve the generalisation performance in cases where only limited training data is available. This paper examines the performance of both types of classifiers in two-class fault/no-fault recognition examples and the attempts to improve the overall generalisation performance of both techniques through the use of genetic algorithm based feature selection process. (C) 2002 Published by Elsevier Science Ltd.