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Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

机译:基于动态时间规整的定子电流信号分析电机驱动故障诊断

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摘要

Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency component as that of the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time domain in comparison with conventional FT based methods.
机译:电动机定子电流信号已被广泛用于监视感应电机及其下游机械设备的状况。用于电流信号分析的关键技术基于傅立叶变换(FT),从以电源频率分量及其高阶谐波为主的信号中提取弱故障边带分量。但是,基于FT的方法具有诸如频谱泄漏和混叠之类的局限性,导致在估计边带成分时出现重大误差。因此,本文提出使用动态时间规整(DTW)处理电动机电流信号,以检测和量化下游两级往复式压缩机的常见故障。 DTW是一种基于时域的方法,其算法简单且易于嵌入到实时设备中。在这项研究中,DTW用于抑制电源频率分量并基于参考信号的引入来突出显示边带分量,该参考信号具有与电源功率相同的频率分量。此外,设计了滑动窗口以使用DTW逐帧处理原始信号,以进行有效计算。基于提出的方法,分析了压缩机在不同的常见故障和不同负载下感应出的定子电流信号,以进行故障诊断。结果表明,基于通过引入参考信号进行残留信号分析的DTW,可以很好地抑制电源组件,从而突出显示与故障相关的边带组件,以获得准确的故障检测和诊断结果。尤其是,残余信号的均方根(RMS)值可以指示正常情况下在不同排放压力下不同断层之间的差异。与传统的基于FT的方法相比,它提供了一种有效且简便的方法来分析电动机电流信号,以便在时域中更好地诊断电动机驱动器的下游机械设备。

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