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Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes

机译:基于SVM的复杂工业过程故障诊断和过程监控的最新进展

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With the advancement of industrial systems, fault monitoring and diagnosis methods based on the data-driven attract much attention in recent years. This kind of methods are widely used in engineering projects, especially in those big and complicated machines, whose conditions are difficult to obtain from straight view. They can provide the administrator with effective fault information in initial phase and therefore reduce the loss caused by faults. This paper reviews the research and development of fault diagnosis and monitoring approach based on support vector machine (SVM). While many other methods, such as expert system and artificial neural network, have been used in fault monitoring and diagnosis, SVM shows its advantage in generalization performance and in case of small sample. Therefore, it should attract more attention. (C) 2015 Elsevier B.V. All rights reserved.
机译:随着工业系统的发展,基于数据驱动的故障监测和诊断方法近年来引起了人们的广泛关注。这种方法广泛用于工程项目中,特别是在那些大型且复杂的机器中,这些条件很难直接获得条件。它们可以在初始阶段为管理员提供有效的故障信息,从而减少故障造成的损失。本文综述了基于支持向量机(SVM)的故障诊断与监测方法的研究与发展。虽然许多其他方法(例如专家系统和人工神经网络)已用于故障监视和诊断,但SVM在泛化性能和小样本情况下显示出其优势。因此,应该引起更多关注。 (C)2015 Elsevier B.V.保留所有权利。

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