A new procedure for extracting fault feature of multi-frequency signal from rotating machinery
A new procedure for extracting fault feature of multi-frequency signal from rotating machinery
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DOI：
10.1016/j.ymssp.2012.06.015
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发表时间：
2012-10
影响因子：
8.4
通讯作者：
Xin Xiong;Shixi Yang;C. Gan
中科院分区：
文献类型：
--
作者：
Xin Xiong;Shixi Yang;C. Gan
现代旋转机械是多转子多轴承系统，由于碰摩、不对中故障等复杂因素，会导致系统的高度非线性和振动信号的非平稳性。由于这些复杂因素可能产生宽频谱的频率分量，特征提取对于转子系统的故障诊断变得非常重要，例如，转子与定子摩擦和转子不对中。近年来，将经验模式分解（EMD）算法与希尔伯特变换（HT）相结合的希尔伯特黄变换（HHT）被广泛应用于振动信号分析，并被证明在处理非平稳信号方面非常有效。然而，大多数固有模式函数（IMF）从EMD是多频，和提取的瞬时频率（IF）曲线通常显示不规则性，这增加了困难，解释这些功能的信号的HHT谱图。在这项研究中，一个新的程序，结合传统的HHT与四阶谱分析工具Kurtogram，开发了从几种故障信号中提取高频特征，其中Kurtogram被应用于定位非平稳的波内和波间调制分量在原始信号中，并产生更多的单色IMF。通过转子-轴承组件碰摩试验和汽轮压缩机组不对中试验，验证了该方法的有效性。
Modern rotating machinery is built as a multi-rotor and multi-bearing system, and complex factors from rub or misalignment fault, etc., can lead to high nonlinearity of the system and non-stationarity of vibration signals. As a wide spectrum of frequency components is likely generated due to these complex factors, feature extraction becomes very important for fault diagnosis of a rotor system, e.g., rotor-to-stator rub and rotor misalignment. In recent years, the Hilbert–Huang transform (HHT), combining the empirical mode decomposition (EMD) algorithm with the Hilbert transform (HT) is commonly used in vibration signal analysis and also turns out to be very effective in dealing with non-stationary signals. Nevertheless, most intrinsic mode functions (IMFs) from the EMD are multi-frequency, and the extracted instantaneous frequency (IF) curves usually show irregularities, which raises difficulty in interpreting these features of the signal by the HHT spectrogram. In this study, a new procedure, combining the customary HHT with a fourth-order spectral analysis tool named Kurtogram, is developed to extract high-frequency features from several kinds of faulty signals, where the Kurtogram is applied to locate the non-stationary intra- and inter-wave modulation components in the original signals and produce more monochromatic IMFs. It is shown that the newly developed feature extraction procedure can accurately detect and characterize the fault feature information hidden in a multi-frequency signal, which is validated by a rub test from a rotor-bearing assembly and a misalignment signal test from a turbo-compressor machine set.