基于FastICA去噪和改进HHT的转子不平衡故障特征提取方法研究

针对转子不平衡振动信号非平稳性并伴随较强环境噪声的特点,提出一种基于快速独立分量分析(FastICA)和改进希尔伯特-黄变换相结合的故障特征提取方法。该方法采用FastICA法去除环境噪声等因素对于故障特征提取精度的影响,再利用自适应白噪声总体平均模态分解方法将故障信号分解为一系列固有模态函数(IMF),并采用基于相似性评估的虚假IMF选择算法将与故障信息无关的虚假IMF分量剔除,从而保证故障信息提取的准确性和有效性。通过仿真分析证明了所提方法的有效性,并且实际试验表明:该方法可有效提取转子不平衡信号的故障特征,为该类故障的诊断提供了一种切实可行的方法。

Research on Fault Feature Extraction Method of Rotor Imbalance Based on FastICA Denoising and Improved HHT

The rotor imbalance vibration signal is proved to be non stationary and carries intense ambient noise. In view of these characteristics, a fault feature extraction method based on fast independent component analysis (FastICA) and improved Hilbert-Huang transform (HHT) was proposed. FastICA method was used to eliminate ambient noise and other factors of the signals, then the noise reduction signal was decomposed by complete ensemble empirical mode decomposition withe adaptive noise (CEEMDAN) and a set of intrinsic mode function (IMF) were obtained. The illusive component selection technique based on correlation analysis was used to decide which the illusive component was and the accuracy of fault information extraction was guaranteed. The simulation experiment results show the effectiveness of the proposed method. The actual test shows that the proposed method can be used to extract the information of rotor imbalance fault signal effectively and accurately. It provides an effective method for this problem.