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

机译：基于模糊神经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：准确的检测/诊断初始故障是关键的，用于确保电动机系统的安全性和可靠性，因为这些故障可能导致严重的性能下降甚至最终的故障，如果没有正确检测和维护。在本文中，我们首先使用神经网络介绍一种新的模糊逻辑系统近似方法。基于“等级”模糊会员函数的呈现，任何模糊关系都可以通过神经网络映射来近似。借助于这种方法，然后呈现改进的自适应神经模糊推理系统（ANFIS），其被证明是用模糊输出信息处理。最终提出了改进的基于ANFIS的电机故障诊断方案。我们的故障诊断计划具有直接学习监督专家的优势。此外，获得的诊断结果可以很容易地以语言方式解释。轴承故障诊断问题在此作为该方案的测试平台。进行计算机模拟以证明其有效性。

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