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国外文献 > 1.轴承 > 8. 系统与应用 > 电机与旋转机械

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标 题： A Frequency-Based Approach to Detect Bearing Faults in Induction Motors Using Discrete Wavelet Transform
基于频率的感应电机轴承故障检测方法：利用离散小波变换
作 者： Amirhossein Ghods
Hong-hee Lee
关键词： Induction motor  [感应电机]
Fault diagnosis  [ 故障诊断]
Fourier transform  [ 傅里叶变换]
Wavelet transform  [ 小波变换]
Frequency-spectrum  [ 频率-频谱]
Non-stationary fault  [ 非稳态故障]
出版年： 2014
会议名： 2014 IEEE International Conference on Industrial Technology (ICIT 2014)
2014年IEEE国际工业技术会议（ICIT 2014）
届 次：
摘 要： Detection of faults in induction motors is nowadays is a hot trend in the field of electrical machinery. There are several methods to detect electrical and mechanical faults in an asynchronous motor; fast Fourier transform, short-time Fourier transform, and wavelet transform are the most popular ones. A major deficiency that most of these solutions face is not being able to detect low energy faults, such as mechanical bearing faults. The new solution proposed in this paper focuses on detection and prediction of low energy faults applying discrete wavelet transform (DWT); the output signal is passed through high pass and low pass filters and coefficients are derived consequently. The method offered by the authors of this paper includes deriving frequency spectrum of each level of discretization. Especially in high decomposition levels, inner race bearing faults can be detected earlier by monitoring frequency spectrum of high levels in DWT.

如今，感应电机故障检测已成为电气机械领域的一个热门趋势。异步电机的电气和机械故障检测有多种方法； 快速傅里叶变换、短时傅里叶变换和小波变换是最常用的方法。这些方法大多存在一个主要缺陷，即无法检测低能量故障，例如机械轴承故障。本文提出的新方法聚焦于利用离散小波变换（DWT）来检测和预测低能量故障； 输出信号经过高通和低通滤波器处理，从而得到相应的系数。本文作者提出的方法包括对每一级离散化进行频率谱的提取。特别是在高分解层次中，通过监测DWT中的高频谱，可更早地检测出内圈轴承故障。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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