Wang Zhijian, Wang Junyuan, Zhao Zhifang, Wang Rijun
School of Mechanical and Power Engineering, North University of China, Xueyuan Road, Taiyuan 030051, China.
Entropy (Basel). 2017 Dec 26;20(1):10. doi: 10.3390/e20010010.
Strong background noise and complicated interfering signatures when implementing vibration-based monitoring make it difficult to extract the weak diagnostic features due to incipient faults in a multistage gearbox. This can be more challenging when multiple faults coexist. This paper proposes an effective approach to extract multi-fault features of a wind turbine gearbox based on an integration of minimum entropy deconvolution (MED) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA). By using simulated periodic transient signals with different noise to signal ratios (SNR), it evaluates the outstanding performance of MED in noise suppression and reveals the deficient in extract multiple impulses. On the other hand, MOMEDA can performs better in extracting multiple pulses but not robust to noise influences. To compromise the merits of them, therefore the diagnostic approach is formalized by extracting the multiple weak features with MOMEDA based on the MED denoised signals. Experimental verification based on vibrations from a wind turbine gearbox test bed shows that the approach allows successful identification of multiple faults occurring simultaneously on the shaft and bearing in the high speed transmission stage of the gearbox.
在基于振动的监测中，强烈的背景噪声和复杂的干扰信号使得在多级齿轮箱中由于早期故障而难以提取微弱的诊断特征。当多个故障共存时，这可能更具挑战性。本文提出了一种基于最小熵反卷积（MED）和多点最优最小熵反卷积调整（MOMEDA）相结合的有效方法来提取风力发电机组齿轮箱的多故障特征。通过使用具有不同信噪比（SNR）的模拟周期性瞬态信号，评估了MED在噪声抑制方面的出色性能，并揭示了其在提取多个脉冲方面的不足。另一方面，MOMEDA在提取多个脉冲方面表现更好，但对噪声影响不鲁棒。因此，为了兼顾它们的优点，通过基于MED去噪信号用MOMEDA提取多个微弱特征来规范诊断方法。基于风力发电机组齿轮箱试验台振动的实验验证表明，该方法能够成功识别齿轮箱高速传动阶段轴和轴承上同时出现的多个故障。
