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Fuzzy-Neuro Technique-based Intelligent Fault Diagnosis in Electrical Motor Systems

机译：基于模糊神经网络技术的电动机系统智能故障诊断

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Accurate detection/diagnosis of incipient faults is pivotal in assuring the safety and reliability of electrical motor systems, since these faults can lead to serious performance degradation or even eventual failures, if not properly detected and maintained. In this paper, we first introduce a new fuzzy logic system approximation method using neural networks. Based on the 'grade' presentation of fuzzy membership functions, any fuzzy relationship can be approximated by a neural network mapping. With the aid of this approach, a modified Adaptive Neural Fuzzy Inference System (ANFIS) is then presented, which is proven suitable to handle with fuzzy output information. The modified ANFIS-based motor fault diagnosis scheme is finally proposed. Our fault diagnosis scheme has the advantage of learning directly from supervising experts. In addition, the diagnosis results acquired can be easily interpreted in a linguistic way. The bearing fault diagnosis problem is employed here as a testbed for this scheme. Computer simulations are carried out to demonstrate its effectiveness.
机译：早期故障的准确检测/诊断对于确保电动机系统的安全性和可靠性至关重要，因为如果不正确检测和维护，这些故障可能会导致严重的性能下降甚至最终故障。在本文中，我们首先介绍一种使用神经网络的模糊逻辑系统近似方法。基于模糊隶属度函数的“等级”表示，可以通过神经网络映射来近似任何模糊关系。然后，借助这种方法，提出了一种改进的自适应神经模糊推理系统（ANFIS），该系统被证明适合处理模糊输出信息。最后提出了改进的基于ANFIS的电机故障诊断方案。我们的故障诊断方案具有直接从监督专家那里学习的优势。另外，所获得的诊断结果可以以语言方式容易地解释。此处将轴承故障诊断问题用作该方案的测试平台。进行计算机模拟以证明其有效性。

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