A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox. (July 2021) - Zhang, Kai; Tang, Baoping; Deng, Lei; Liu, Xiaoli
Highlights: The fault diagnosis framework combines the wavelet transform and the ResNet. Frequency attention highlights weak but crucial features in wavelet coefficients. Channel attention improves the representation of the learned feature-map. Hybrid attention-based method boosts ResNet's performance in fault diagnosis. Abstract: It is significant to boost the performance of fault diagnosis of wind turbine gearboxes. In this paper, a hybrid attention improved residual network (HA-ResNet) based method is proposed to diagnose the fault of wind turbines gearbox by highlighting the essential frequency bands of wavelet coefficients and the fault features of convolution channels. First, the paper performed wavelet packet transformation (WPT) on the raw signal and improved the ResNet by the band attention to highlight features of wavelet coefficients. Second, a fault diagnosis framework based on channel attention is designed to effectively improve the nonlinear feature extraction ability of deep convolutional networks. The proposed method is verified by a simulation dataset of the drivetrain diagnostic simulator (DDS) and the measured data from a wind farm. The results illustrate the superior performance of the HA-ResNet based fault diagnosis method for time–frequency feature extraction of vibration signals, frequency band information enhancement, and recognition accuracy improvement.