Sun Huer, Wu Chao, Liang Xiaohua, Zeng Qunfeng
The School of Mechanical Engineering, North University of China, Xueyuan Road, Taiyuan 030051, China.
Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an 710049, China.
Entropy (Basel). 2018 Nov 6;20(11):850. doi: 10.3390/e20110850.
The weak compound fault feature is difficult to extract from a gearbox because the signal components are complex and inter-modulated. An approach (that is abbreviated as MRPE-MOMEDA) for extracting the weak fault features of a transmission based on a multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) and the permutation entropy was proposed to solve this problem in the present paper. The complexity of the periodic impact signal was low and the permutation entropy was relatively small. Moreover, the amplitude of the impact was relatively large. Based on these advantages, the multipoint reciprocal permutation entropy (MRPE) was proposed to track the impact fault source of the weak fault feature in gearbox compound faults. The impact fault period was indicated through MRPE. MOMEDA achieved signal denoising. The optimal filter coefficients were solved using MOMEDA. It exhibits an outstanding performance for noise suppression of gearbox signals with a periodic impact. The results from the transmission show that the proposed method can identify multiple faults simultaneously on a driving gear in the 4th gear of the transmission.
由于信号成分复杂且存在互调现象，从齿轮箱中提取微弱复合故障特征较为困难。本文提出了一种基于多点最优最小熵反卷积调整（MOMEDA）和排列熵来提取变速器微弱故障特征的方法（简称为MRPE-MOMEDA），以解决这一问题。周期性冲击信号的复杂度较低，排列熵相对较小。此外，冲击的幅度相对较大。基于这些优点，提出了多点互逆排列熵（MRPE）来跟踪齿轮箱复合故障中微弱故障特征的冲击故障源。通过MRPE指示冲击故障周期。MOMEDA实现了信号去噪。使用MOMEDA求解最优滤波器系数。它在抑制具有周期性冲击的齿轮箱信号噪声方面表现出卓越的性能。变速器的结果表明，所提出的方法能够同时识别变速器四档主动齿轮上的多个故障。
