基于计算智能的在线状态监测

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摘要翻译：
提出了采用多层感知器(MLP)、径向基函数(RBF)和支持向量机(SVM)分类器的套管状态监测框架。框架的第一级确定衬套是否有故障，而第二级确定故障的类型。用溶解气体分析法对衬套中的诊断气体进行了分析。MLP在精度和训练时间方面都优于SVM和RBF。此外，介绍了一种能够适应新采集数据的套管状态在线监测方法。该方法能够适应由输入数据引入的新类，并使用使用MLP的增量学习算法来实现。随着新数据的引入，测试结果由67.5%提高到95.8%，随着新条件的引入，测试结果由60%提高到95.3%。该框架对其决策的平均置信度为0.92。
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英文标题：
《On-Line Condition Monitoring using Computational Intelligence》
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作者：
C.B. Vilakazi, T. Marwala, P. Mautla and E. Moloto
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最新提交年份：
2007
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  This paper presents bushing condition monitoring frameworks that use multi-layer perceptrons (MLP), radial basis functions (RBF) and support vector machines (SVM) classifiers. The first level of the framework determines if the bushing is faulty or not while the second level determines the type of fault. The diagnostic gases in the bushings are analyzed using the dissolve gas analysis. MLP gives superior performance in terms of accuracy and training time than SVM and RBF. In addition, an on-line bushing condition monitoring approach, which is able to adapt to newly acquired data are introduced. This approach is able to accommodate new classes that are introduced by incoming data and is implemented using an incremental learning algorithm that uses MLP. The testing results improved from 67.5% to 95.8% as new data were introduced and the testing results improved from 60% to 95.3% as new conditions were introduced. On average the confidence value of the framework on its decision was 0.92.
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PDF链接：
https://arxiv.org/pdf/0705.2310

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关键词：计算智能 在线状态 Intelligence Presentation Computation condition RBF 使用 气体