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摘要翻译：
本文提出了一种利用Takagi-Sugeno神经模糊模型和由圆柱壳振动信号计算的伪峰能量进行故障分类的方法。为了状态监测的目的，计算伪峰能量是从振动信号中提取特征的一种精确方法。因此，该计算用于从不同圆柱壳群体获得的振动信号中提取特征。种群中的一些圆柱体在不同的子结构中存在断层。然后，从振动信号中计算出的伪峰能量被用作神经模糊模型的输入。使用留一交叉验证过程来测试模型的性能。结果表明，神经模糊模型对故障的分类准确率为91.62%，高于以往的多层感知器。
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英文标题：
《Fault Classification using Pseudomodal Energies and Neuro-fuzzy
modelling》
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作者：
Tshilidzi Marwala, Thando Tettey and Snehashish Chakraverty
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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 a fault classification method which makes use of a Takagi-Sugeno neuro-fuzzy model and Pseudomodal energies calculated from the vibration signals of cylindrical shells. The calculation of Pseudomodal Energies, for the purposes of condition monitoring, has previously been found to be an accurate method of extracting features from vibration signals. This calculation is therefore used to extract features from vibration signals obtained from a diverse population of cylindrical shells. Some of the cylinders in the population have faults in different substructures. The pseudomodal energies calculated from the vibration signals are then used as inputs to a neuro-fuzzy model. A leave-one-out cross-validation process is used to test the performance of the model. It is found that the neuro-fuzzy model is able to classify faults with an accuracy of 91.62%, which is higher than the previously used multilayer perceptron.
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PDF链接：
https://arxiv.org/pdf/0705.2236
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