Zhang Ziying, Zhang Xi, Zhang Panpan, Wu Fengbiao, Li Xuehui
School of Mechanical, Electronic and Information Engineering, China University of Mining and Technology (CUMT), Xueyuan Road, Beijing 100083, China.
Shanxi Institute of Energy, Daxue Road, Jinzhong 030600, China.
Entropy (Basel). 2018 Dec 26;21(1):18. doi: 10.3390/e21010018.
Dual-tree complex wavelet transform has been successfully applied to the composite diagnosis of a gearbox and has achieved good results. However, it has some fatal weaknesses, so this paper proposes an improved dual-tree complex wavelet transform (IDTCWT), and combines minimum entropy deconvolution (MED) to diagnose the composite fault of a gearbox. Firstly, the number of decomposition levels and the effective sub-bands of the DTCWT are adaptively determined according to the correlation coefficient matrix. Secondly, frequency mixing is removed by notch filter. Thirdly, each of the obtained sub-bands further reduces the noise by minimum entropy deconvolution. Then, the proposed method and the existing adaptive noise reduction methods, such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and variational mode decomposition (VMD), are used to decompose the two sets of simulation signals in comparison, and the feasibility of the proposed method has been verified. Finally, the proposed method is applied to the compound fault vibration signal of a gearbox. The results show the proposed method successfully extracts the outer ring fault at a frequency of 160 Hz, the gearbox fault with a characteristic frequency of 360 Hz and its double frequency of 720 Hz, and that there is no mode mixing. The method proposed in this paper provides a new idea for the feature extraction of a gearbox compound fault.
双树复数小波变换已成功应用于齿轮箱的复合故障诊断并取得了良好效果。然而，它存在一些致命弱点，因此本文提出一种改进的双树复数小波变换（IDTCWT），并结合最小熵反卷积（MED）来诊断齿轮箱的复合故障。首先，根据相关系数矩阵自适应确定双树复数小波变换的分解层数和有效子带。其次，通过陷波滤波器消除频率混叠。第三，对得到的每个子带进一步采用最小熵反卷积降低噪声。然后，将所提方法与现有的自适应降噪方法，如经验模态分解（EMD）、总体经验模态分解（EEMD）和变分模态分解（VMD），用于对比分解两组仿真信号，验证了所提方法的可行性。最后，将所提方法应用于齿轮箱的复合故障振动信号。结果表明，所提方法成功提取出了频率为160Hz的外圈故障、特征频率为360Hz及其倍频720Hz的齿轮箱故障，且不存在模态混叠现象。本文所提方法为齿轮箱复合故障特征提取提供了新思路。
