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An Improved Nonparametric Method for Fault Detection of Induction Motors Based on the Statistics of the Fractional Moments

机译：基于分数矩统计的改进型非参数感应电动机故障检测方法

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摘要

Accurate detection of faults of induction motors is an important challenge for industry. Often only stator currents/voltages are measurable and, hence, can be analyzed for fault detection. Among current analysis methods, the most attractive ones have low computational cost and little memory requirement for treatment of the measured data. Motor current signature analysis based on Fast Fourier Transform (FFT) provides fast computation, however it requires a long acquisition time interval for accurate fault detection. In fact, a short acquisition time causes the available frequency resolution of FFT spectrum to decrease and, hence, the spectra of the healthy and broken motors become indistinguishable. Recently, a statistical method, the so-called Statistics of Fractional Moments (SFM) allowed to distinguish signals with small differences. The statistically “close” signals are clustered and separated from “anomalous” signals. In this paper, the FFT and SFM are combined to reduce the acquisition time interval and hence the required memory and computation, while the fault detection capability is preserved. The effectiveness of the proposed approach is tested both on a healthy induction motor and on the same motor with broken rotor bars, for different acquisition time intervals.
机译：准确检测感应电动机的故障是工业上的重要挑战。通常只有定子电流/电压是可测量的，因此可以进行分析以进行故障检测。在当前的分析方法中，最吸引人的分析方法具有较低的计算成本和很少的用于处理测量数据的存储需求。基于快速傅立叶变换（FFT）的电动机电流签名分析可提供快速计算，但是需要较长的采集时间间隔才能进行准确的故障检测。实际上，较短的采集时间会导致FFT频谱的可用频率分辨率降低，因此，正常电机和故障电机的频谱将变得难以区分。最近，一种统计方法，即所谓的分数矩统计（SFM），可以区分具有微小差异的信号。统计上“接近”的信号被聚类并与“异常”信号分开。本文将FFT和SFM相结合以减少采集时间间隔，从而减少了所需的存储和计算量，同时保留了故障检测能力。在健康的感应电动机和转子条损坏的同一电动机上，针对不同的采集时间间隔，都测试了该方法的有效性。

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