基于双树复小波包变换能量泄漏特性分析的齿轮故障诊断

为有效利用双树复小波包变换提取齿轮故障特征信息,提出基于双树复小波包能量泄漏特性分析的故障诊断方法。首先根据高斯白噪声频率充满整个频带的特性,通过双树复小波包变换对高斯白噪声进行分解,利用频带能量泄漏的定量分析方法,验证了双树复小波包变换具有较低的频带能量泄漏特性;其次利用双树复小波包变换逐层分解信号,对每层分解所得分量求其FFT谱的峭度,得到基于双树复小波包变换的谱峭度图,根据图中峭度最大的原则,可以自动准确的选择信号分解最佳层数和最佳分量;最后将基于双树复小波包变换的谱峭度图的故障诊断方法应用于实际工程中,对齿轮故障振动信号进行分析,选择最佳分解层数和分量后利用希尔伯特包络解调,有效准确地提取了故障特征信息,验证了方法的可行性和有效性。该研究可为旋转机械设备中齿轮箱故障诊断的故障特征提取提供参考。

Analysis of energy leakage characteristics of dual-tree complex wavelet packet transform and its application on gear fault diagnosis

Abstract: The gear is the key component of rotating machinery, so a fault in the gear will directly affect the condition of the whole machine's operation. It was difficult to extract the fault feature information effectively from the vibration signals of a faulty gear. In the field of fault diagnosis, envelope demodulation was one of the most common signal processing methods. However, a filtering process was required before envelope demodulation. The parameters of a filter were determined by experience, and that has a great influence on the results of signal processing. The discrete wavelet packet transform has a larger energy leakage of frequency band, which obviously affected the results of the envelope demodulation. It is necessary to have a method with a lower energy leakage of the frequency band before envelope demodulation. The dual tree complex wavelet packet transform (DT-CWPT) was a new signal processing method that had many good qualities. Because the energy leakage of the frequency band was smaller when the signal was decomposed by a dual tree complex wavelet packet transform, the dual tree complex wavelet packet transform was used to extract the fault feature information in the field of fault diagnosis. In this paper, first, according to the characteristics of Gaussian white noise, whose frequency was full of the whole frequency band, Gaussian white noise was decomposed by a dual-tree complex wavelet packet transform, and the parts with energy leakage were regarded as a theoretical part band beyond the range of the frequency components. Then the lower energy leakage characteristic of dual tree complex wavelet packet transform was verified by a quantitative analysis method of frequency band energy leakage. A dual tree complex wavelet packet transform has an advantage in the pretreatment of envelope demodulation compared with the method of discrete wavelet packet transform. Secondly, the signal was decomposed layer-by-layer by a dual tree complex wavelet packet transform, and the kurtogram based on a dual tree complex wavelet packet transform could be obtained by computing the spectral kurtosis of every layer's components. According to the standard of maximum kurtosis, the layer of decomposition and the component about the signal can be chosen automatically and accurately. The best layer of the dual tree complex wavelet packet decomposition was the layer of the maximum kurtosis and the component which had the maximum kurtosis was the best component of decomposition. Finally, the vibration signal of the engineering was processed by the method of spectral kurtosis based on a dual tree complex wavelet packet transform, the best decomposition layer and component could be chosen, and the fault feature information was extracted effectively by a Hilbert envelope demodulation, where the feasibility and effectiveness of the method were verified. The research will provide a reference for extracting the fault feature information of a gearbox fault diagnosis in rotating machinery.