Efficient and accurate planetary gearbox fault diagnosis is the key to enhance the reliability and security of wind turbines. Therefore, an intelligent and integrated approach based on deep belief networks (DBNs), improved logistic Sigmoid (Isigmoid) units and impulsive features is proposed in this paper. Vanishing gradient problem is an inherent drawback of conventional Sigmoid units, which usually occurs in the back-propagation process of DBNs, resulting in that learning considerably slows down and the classification rate is reduced. To solve this problem, Isigmoid units are designed to combine the merits of unsaturation from Leaky Rectified Linear (LReL) units. The results of handwritten digit recognition experiments show the superiority of Isigmoid over Sigmoid on convergence speed and classification accuracy. Since impulses contain much useful fault information, especially for early failures, an integrated approach using the optimized Morlet wavelet transform, kurtosis index and soft-thresholding is appliedto extract impulse components from original signals to improve the diagnosis accuracy. Then the features extracted from original signals and impulsive signals are employed to train and test the DBNs with Isigmoid, Sigmoid and LReL units for comparison. Finally, the results of planetary gearbox fault diagnosis show that Isigmoid has higher comprehensive performance than conventional sigmoid and LReL. In future, the Python code will be provided.
For the detail, please see: Yi Qin*, Xin Wang, Jingqiang Zou. The optimized deep belief networks with improved logistic Sigmoid units and their application in fault diagnosis for planetary gearboxes of wind turbines. IEEE Transactions on Industrial Electronics, 2019, 66(5): 3814-3824.