CAJ | 학술논문

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。
위유효이용쌍수복소파포변환제취치륜고장특정신식，제출기우쌍수복소파포능량설루특성분석적고장진단방법。수선근거고사백조성빈솔충만정개빈대적특성，통과쌍수복소파포변환대고사백조성진행분해，이용빈대능량설루적정량분석방법，험증료쌍수복소파포변환구유교저적빈대능량설루특성；기차이용쌍수복소파포변환축층분해신호，대매층분해소득분량구기FFT보적초도，득도기우쌍수복소파포변환적보초도도，근거도중초도최대적원칙，가이자동준학적선택신호분해최가층수화최가분량；최후장기우쌍수복소파포변환적보초도도적고장진단방법응용우실제공정중，대치륜고장진동신호진행분석，선택최가분해층수화분량후이용희이백특포락해조，유효준학지제취료고장특정신식，험증료방법적가행성화유효성。해연구가위선전궤계설비중치륜상고장진단적고장특정제취제공삼고。
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.