Fault diagnosis of brushless direct current (BLDC) motors has recently gained much attention. Interturn faults in BLDC motor distort three-phase currents waveforms, leading to an asymmetrical condition. In this article, we introduce a simple and efficient method to detect interturn faults based on one modal current and four different simple indices. The modal current is derived by proper linear mixing of measured three-phase currents. Following the initial processing of the modal current, three main indices, including moving mean, variance, and signal energy, are obtained in parallel. Also, an auxiliary correlation-based index is suggested to enhance the method for discrimination of faulty conditions from healthy ones. The fault detection is made by passing at least two main indices (out of three indices), and also an auxiliary index from a predefined threshold. The proposed technique is evaluated under different loads, speeds, and fault severities in two different datasets: data from a simulated motor in Maxwell finite element package, and a real four-pole motor. Moreover, the method is compared with other methods from different aspects. The results confirm a high accuracy and quick fault detection in the proposed approach.

中文翻译：

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一种简单有效的基于电流的BLDC电动机匝间故障检测方法

无刷直流（BLDC）电动机的故障诊断近来引起了很多关注。BLDC电机的匝间故障会使三相电流波形失真，从而导致不对称状态。在本文中，我们介绍了一种基于一种模态电流和四种不同的简单指标来检测匝间故障的简单有效的方法。模态电流是通过对测得的三相电流进行适当的线性混合而得出的。在对模态电流进行初始处理之后，并行获得了三个主要指标，包括移动平均值，方差和信号能量。此外，建议使用基于辅助相关性的索引来增强从健康状况中区分故障状况的方法。通过传递至少两个主要索引（三个索引中的一个）来进行故障检测，以及来自预定义阈值的辅助索引。在两个不同的数据集中，在不同的负载，速度和故障严重性下，对所提出的技术进行了评估：来自Maxwell有限元软件包中的仿真电动机的数据和实际的四极电动机。此外，将该方法与来自不同方面的其他方法进行了比较。结果证实了所提出方法的高精度和快速故障检测。