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Mechanical fault diagnosis of induction motor using Hilbert pattern

机译:基于希尔伯特模式的异步电动机机械故障诊断

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This paper deals with mechanical fault diagnosis in three-phase induction motor from radial vibration measurement. The Hilbert pattern of the 50 Hz mono-component signal extracted from the steady state vibration signature is analyzed and found to contain useful information needed for diagnosing different mechanicals faults. Since Hilbert transform can only be applied to a mono-component signal, Kaiser windowed FIR band pass filter is used to extract the monocomponent signal. Complex analytic signal is generated by using the mono-component signal as the real part and it's Hilbert Transform as the imaginary part. The concept of Hilbert transform for extraction of the instantaneous amplitude and frequency is utilized to extract important fault information from the non-stationary vibration signal and found to be quite efficient. This method does not require the analysis of fault frequency components which are slip dependent. Finally, an automatic diagnosis algorithm is attempted using SVM. The proposed method is almost independent of loading condition of the motor and has consistent performance even in presence of high level of noise.
机译:本文从径向振动测量的角度对三相异步电动机的机械故障进行诊断。分析了从稳态振动信号中提取的50 Hz单分量信号的希尔伯特模式,发现该希尔伯特模式包含诊断不同机械故障所需的有用信息。由于希尔伯特变换只能应用于单分量信号,因此使用Kaiser窗口FIR带通滤波器来提取单分量信号。复杂的分析信号是通过将单分量信号作为实部并使用希尔伯特变换作为虚部来生成的。利用希尔伯特变换的概念提取瞬时幅度和频率,可以从非平稳振动信号中提取重要的故障信息,并且效率很高。该方法不需要分析与转差有关的故障频率分量。最后,尝试使用SVM进行自动诊断算法。所提出的方法几乎与电动机的负载条件无关,并且即使在高噪声水平下也具有一致的性能。

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