Rotor failure detection of induction motors by wavelet transform and Fourier transform in non-stationary condition

Case Studies in Mechanical Systems and Signal Processing Pub Date : 2015-07-01 DOI:10.1016/j.csmssp.2015.05.001

Cesar da Costa , Masamori Kashiwagi , Mauro Hugo Mathias

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引用次数: 61

Abstract

This case study presents two diagnostic methods for the detection of broken bars in induction motors with squirrel-cage type rotors: FFT method and wavelet method. The FFT method allows detecting broken rotor bar when the motor operates under a load, but if the machine is decoupled from the mechanical load, the side band components associated with broken bars do not appear. The WT is a powerful signal-processing tool used in power systems and other areas. New wavelet-based detection methods that are focused on the analysis of the startup current have been proposed for the detection of broken bars. Since the transient stator current signal is not periodic, it is not amenable to analyze the signal by FFT method. In addition, it is impossible to estimate the time of the fault occurrence using the FFT. In this paper, our main goal is to find out the advantages of wavelet transform method compared to Fourier transform method in rotor failure detection of induction motors.

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基于小波变换和傅立叶变换的异步电动机转子故障检测

本文介绍了鼠笼式转子异步电动机断条的两种诊断方法:FFT法和小波法。当电机在负载下运行时，FFT方法允许检测转子断条，但如果机器与机械负载解耦，则与断条相关的侧带组件不会出现。小波变换是一种用于电力系统和其他领域的强大的信号处理工具。本文提出了一种新的基于小波的检测方法，该方法的重点是对启动电流的分析。由于暂态定子电流信号不具有周期性，因此不适合用FFT方法分析该信号。此外，用FFT估计故障发生的时间是不可能的。在本文中，我们的主要目的是找出小波变换方法相对于傅里叶变换方法在感应电机转子故障检测中的优势。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Case Studies in Mechanical Systems and Signal Processing

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