Purpose

On-time fault diagnosis in electrical machines is a critical issue, as it can prevent the development of fault and also reduce the repairing time and cost. In brushless synchronous generators, the significance of the fault diagnosis is even more because they are widely used to generate electrical power all around the world. Therefore, this study aims to propose a fault detection approach for the brushless synchronous generator. In this approach, a novel extension of Relief feature selection method is developed.

Design/methodology/approach

In this paper, by taking the advantages of the finite element method (FEM), a brushless synchronous machine is modeled to evaluate the machine performance under two conditions. These conditions include the normal condition of the machine and one diode open-circuit of the rotating rectifier. Therefore, the harmonic behavior of the terminal voltage of the machine is obtained under these situations. Then, the harmonic components are ranked by using the extension of Relief to extract the most appropriate components for fault detection. Therefore, a fault detection approach is proposed based on the ranked harmonic components and support vector machine classifier.

Findings

The proposed diagnosis approach is verified by using an experimental test. Results show that by this approach open-circuit fault on the diode rectifier can effectively be detected by the accuracy of 98.5% and by using five harmonic components of the terminal voltage [1].

Originality/value

In this paper, a novel feature selection method is proposed to select the most effective FFT components based on an extension of Relief method, and besides, FEM modeling of a brushless synchronous generator for normal and one diode open-circuit fault.

中文翻译：

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基于数值模型的无刷同步发电机诊断

目的

电机的及时故障诊断是一个关键问题，因为它可以防止故障的发展，并减少维修时间和成本。在无刷同步发电机中，故障诊断的意义甚至更大，因为它们已被全世界广泛地用于发电。因此，本研究旨在提出一种用于无刷同步发电机的故障检测方法。用这种方法，开发了一种新的扩展特征选择方法。

设计/方法/方法

本文利用有限元方法（FEM）的优势，对无刷同步电机建模，以评估两种条件下的电机性能。这些条件包括机器的正常条件和旋转整流器的一个二极管开路。因此，在这些情况下可以获得机器端电压的谐波特性。然后，通过使用Relief的扩展对谐波分量进行排序，以提取最合适的分量进行故障检测。因此，提出了一种基于排序谐波分量和支持向量机分类器的故障检测方法。

发现

通过实验测试验证了所提出的诊断方法。结果表明，通过这种方法，可以以98.5％的精度和使用端电压的五个谐波分量来有效检测二极管整流器的开路故障[1]。

创意/价值

本文提出了一种新颖的特征选择方法，该方法基于Relief方法的扩展来选择最有效的FFT分量，此外，针对正常和一个二极管开路故障，对无刷同步发电机进行了有限元建模。