The deep learning-based fault diagnosis approaches have shown great advantages in ensuring rotating machinery (RM) work normally and safely. However, in real industrial applications, due to the influence of speed fluctuation, the differences in distribution of training samples and testing samples are inevitable, which will greatly affect the diagnosis result of the model. In the article, a novel end-to-end method for the fault transfer diagnosis of RM under time-varying speeds, named semisupervised subdomain adaptation graph convolutional network (SSAGCN) is proposed by integrating SSA and GCN. To begin with, a feature extractor based on GCN is designed to obtain the transferable information of the source domain (SD) and target domain (TD) data. In addition, the closely watched fault diagnosis (FD) approach based on global domain shift and unsupervised domain adaptation is improved by employing an adaptive layer based on SSA to reduce the distribution difference of the same fault type in SD and TD. The proposed SSAGCN approach takes full advantage of the powerful capability of GCN in capturing the relationship between signals, and the excellent performance of SSA in the use of unlabeled samples and idle labeled samples, thus overcoming the distribution differences caused by speed fluctuation. Two experimental cases are carried out to prove its effectiveness under time-varying speeds, and their results indicate our presented SSAGCN approach can realize more excellent performance on diagnosis accuracy and model complexity compared with existing methods.

The recurrence plot (RP) method has been introduced into bearing fault diagnosis due to its capability of effectively analyzing nonlinear and non-stationary waveform signals in dynamic systems. However, the interference of noise increases the difficulty of RP-based fault diagnosis. To solve this problem, this paper proposed a novel anti-noise bearing fault diagnosis method based on improved RP and a convolutional neural network. First, different scales of approximation coefficients and detail coefficients were obtained and constructured for RP based on wavelet packet decomposition (WPD) on the vibrational signal. Meanwhile, redundant parts of each RP were removed according to its symmetry characteristics, and the remaining parts of these RPs were spliced into multi-scale asymmetric RP (MARP) containing all coefficients. Then, a fault diagnosis model for rolling bearing was established with MARP as the input of the pre-trained ResNet-34. Finally, the validity of the proposed fault diagnosis method was validated on the Paderborn bearing dataset. Experimental results showed that the proposed fault diagnosis method achieved an accuracy of 90% under Gaussian white noise with a signal-to-noise ratio of above -6 dB.

Accurate prediction of the remaining useful life (RUL) of mechanical equipment has brought benefits of the company’s reasonable maintenance. However, in real industrial applications, owing to the change of working conditions and the interference of environment noise, it is of great difficulty to extract useful features from the collected signals, making it quite challenging to achieve high-precision RUL prediction of mechanical equipment. In order to overcome these issues, a new high-precision prediction method based on improved convolutional neural network (ICNN), residual attention mechanism with soft thresholding and gated recurrent unit (GRU) is proposed in this paper. To begin with, this method solves the limitations of manual feature extraction and makes the extracted feature representation more obvious by integrating one-dimensional depth separable convolution neural network and two-dimensional transpose convolution neural network. Then, soft thresholding and residual connection is inserted into the attention mechanism to help improve the prediction performance of RUL in noisy environment, making the prediction model adaptively set different thresholds for each sample according to its conditions and characteristics. Finally, the effectiveness of the proposed approach is verified by simulating turbofan engine and IEEE phm2010 data set, indicating that our proposed method has better performance than other approaches of literatures in prediction accuracy and time cost.

Shipborne stabilized platform is an important equipment to ensure the stability of shipborne equipment relative to inertial coordinate system. This paper presents a model predictive control strategy based on ship motion prediction (MPMPC) for ship stabilization platform. Firstly, the ship motion is simulated, and the autoregressive prediction model (AR model) is used to predict the ship motion. Then the kinematics analysis of the Shipborne stabilized platform is carried out and the mathematical model of the hydraulic drive unit (HDU) of the stable platform is established. Then the predicted ship motion is combined with model predictive control (MPC). The predicted trajectory of HDU can be obtained by the kinematics calculation of predicted ship motion. One part of the predicted trajectory is used to compensate the time delay of HDU, and the other part is used as the reference trajectory of the rolling optimization of MPC, instead of the reference trajectory using the measured ship motion at the current moment in traditional model predictive control. Compared with the reference trajectory using the measured ship motion at the current moment, the predicted trajectory of AR model can reflect the future state of the system better, and a better control sequence will be obtained by minimizing the objective function. Finally, the simulation and experiment show that the MPMPC has higher tracking accuracy than traditional MPC.

Disease severity grading is the primary decision-making basis for the amount of pesticide usage in vegetable disease prevention and control. Based on deep learning, this paper proposed an integrated framework, which automatically segments the target leaf and disease spots in cucumber images using different semantic segmentation networks and then calculates the area of disease spots and the target leaf for disease severity grading. Two independent datasets of leaves and lesions were constructed, which served as the training set for the first-stage diseased leaf segmentation and the second-stage lesion segmentation models. The leaf dataset contains 1140 images, and the lesion data set contains 405 images. The proposed TRNet was composed of a convolutional network and a Transformer network and achieved an accuracy of 93.94% by fusing local features and global features for leaf segmentation. In the second stage, U-Net (Resnet50 as the feature network) was used for lesion segmentation, and a Dice coefficient of 68.14% was obtained. After integrating TRNet and U-Net, a Dice coefficient of 68.83% was obtained. Overall, the two-stage segmentation network achieved an average accuracy of 94.49% and 94.43% in the severity grading of cucumber downy mildew and cucumber anthracnose, respectively. Compared with DUNet and BLSNet, the average accuracy of TUNet in cucumber downy mildew and cucumber anthracnose severity classification increased by 4.71% and 8.08%, respectively. The proposed model showed a strong capability in segmenting cucumber leaves and disease spots at the pixel level, providing a feasible method for evaluating the severity of cucumber downy mildew and anthracnose.