The fault diagnosis of the gearbox of wind turbines is a crucial task for wind turbine operation and maintenance. Although a convolutional neural network can extract the related information of adjacent sampling points using kernels, traditional deep learning methods have not leveraged related information from points with a large span of vibration signal data. In this article, a novel fast deep graph convolutional network is proposed to diagnose faults in the gearbox of wind turbines. First, the original vibration signals of the wind turbine gearbox are decomposed by wavelet packet, which presents time–frequency features as graphs. Then, graph convolutional networks are introduced to extract the features of points with a large span of the defined graph samples. Finally, the fast graph convolutional kernel and the particular pooling improvement are used to reduce the number of nodes and achieve fast classification. Experiments on two data sets are performed to verify the efficacy of the proposed method.

This article proposes a new multisensor fusion fault diagnosis method for gearbox, namely residual gated dynamic sparse network, to improve the multisensor feature learning and fusion ability. Considering that the fault sensitivity of the sensor varies with mounted location and complex transfer path modulation causes information from multisensor redundant, the lightweight channel attention unit is designed to strengthen the feature extraction ability of the network. The developed gated dynamic sparse unit is inserted into the deep architecture to eliminate ineffective components caused by high noise interference. Besides, the loss function is improved with multiple activation criteria to enhance convergence ability. The results of experiments and the engineering application show that the proposed method is more effective than other methods under varying degrees of noise interference.

In engineering applications, the effectiveness of rotating machinery fault diagnosis is often disturbed by both vibration noise and labeling noise. Vibration noise makes it more difficult for the model to extract a potential fault impact feature, while labeling noise makes the feature extraction go in the wrong direction through error backpropagation. Both of these factors lead to a decrease in fault diagnosis accuracy. Hence, an adaptive symmetric loss in dynamic wide-kernel ResNet (AS-DWResNet) is proposed to solve the rotating machinery fault diagnosis under noisy labels. Firstly, wider convolution kernels are designed for branch convolution to enhance the extraction of vibration features. Secondly, a parallel network structure is constructed to fully and accurately extract the fault features by dynamically weighting different branches to improve the basic diagnostic performance of the network in the background of vibration noise. Thirdly, an improved adaptive symmetric cross-entropy (ASCE) loss function is adapted to reduce the effect of label noise in training datasets and hyperparameters in loss functions. The experimental results show that in the background of vibration noise and severe noisy labels, the proposed method still keeps more than 99.08% accuracy and has stronger noise robustness and stability than other networks and loss functions.

Data collected from the supervisory control and data acquisition (SCADA) system are widely used in wind farms to detect wind turbines' (WTs) faults. However, it is challenging to extract wind turbines' performance trends and provide accurate alarms based on SCADA data with large operating conditions fluctuations. This article presents a fault detection frame of subspace reconstruction-based robust kernel principal component analysis (SR-RKPCA) model for wind turbines SCADA data to extract nonlinear features under discontinuous interference. First, to improve the stability of the fault detection model of wind turbines, an RKPCA method is developed to decompose the original signal, decomposed into principal component subspace and residual subspace. Second, the permutation entropy is adopted to measure the residual matrix components' disturbance level and remove trendless noise components. Then, a combined index is developed for fault detection with the aid of the residual space matrix with trend information. Ultimately, the proposed SR-RKPCA method for wind turbine detection is verified by investigating wind turbine faults cases. The proposed method has outstanding robustness and a more vital ability to extract nonlinear features for fault detection than traditional principal component analysis (PCA)- or KPCA-based methods.