Wei Zhang’s research while affiliated with University of Shanghai for Science and Technology and other places

Fault diagnosis of industrial processes plays an important role in avoiding heavy losses and ensuring production safety. Complex industrial processes often have many working conditions, and the actual industrial process often concentrates on certain working conditions. As a result, the running time of some working conditions is shorter, so the data of these conditions are difficult to obtain. However, almost all fault diagnosis methods based on Deep Learning (DL) requires a large amount of data. Therefore, it is a big challenge to realize the fault diagnosis of few-shot industrial processes. In order to solve these problems, this paper proposes a Deep Feature Transfer Fusion (DFTF) framework based on hybrid domain transfer learning. The purpose is to take few-shot working conditions as the target domain and carry out fault diagnosis for them. As the features of industrial process images are more complex, this paper introduces Pyramid Hybrid Vision Transformer (PHVT) model, which have stronger feature extraction capabilities and spatial perception, as feature extraction module. In order to improve the transferability of the model, this paper introduces the In-Cross Domain Hybrid Transfer Learning (ICTL) method. By fusing the general object features from ResNet50 which pre-trained under public dataset of common object and the features extracted from PHVT which pre-trained under industrial dataset of multiple working conditions, the adaptability of the model to different scenes is enhanced. The experimental results based on Pronto dataset of Process System Engineering lab of Cranfield University show that the proposed transfer learning method has excellent performance.

When there are obstacles around the target point, the mobile robot cannot reach the target using the traditional artificial potential field (APF). Besides, the traditional APF is prone to local oscillation in complex terrain such as three‐point collinear or semiclosed obstacles. Aiming at solving the defects of traditional APF, a novel improved APF algorithm named back virtual obstacle setting strategy‐APF has been proposed in this paper. There are two main advantages of the proposed method. First, by redefining the gravitational function as a logarithmic function, the proposed method can make the mobile robot reach the target point when there are obstacles around the target. Second, the proposed method can avoid falling into local oscillation for both three‐point collinear and semiclosed obstacles. Compare with APF and other improved APF, the feasibility of the algorithm is proved through software simulation and practical application.

Recently, the application of convolution neural network (CNN) in single image super-resolution (SISR) is gradually developing. Although many CNN-based methods have acquired splendid performance, oversized model complexity hinders their application in real life. In response to this problem, lightweight and efficient are becoming development tendency of SR models. The residual feature distillation network (RFDN) is one of the state-of-the-art lightweight SR networks. However, the shallow residual block (SRB) in RFDN still uses ordinary convolution to extract feature, where still has great improvement room for the reduction of network parameters. In this paper, we propose the Group-convolutional Feature Enhanced Distillation Network (GFEDNet), which is constructed by the stacking of feature distillation and aggregation block (FDAB). Benefitting from residual learning of residual feature aggregation (RFA) framework and feature distillation strategy of RFDN, the FDAB can obtain more diverse and detailed feature representations, thereby improves the SR capability. Furthermore, we propose the multi-scale group convolution block (MGCB) to replace the SRB. Thanks to group convolution and multi-branch parallel structure, the MGCB reduces the parameters substantially while maintaining SR performance. Extensive experiments show the powerful function of our proposed GFEDNet against other state-of-the-art methods.

When there are obstacles around the target point, the mobile robot cannot reach the target using traditional Artificial Potential Field (APF). Besides, the traditional APF is prone to local oscillation in complex terrain such as three-point collinear or semi-closed obstacles. Aiming at solving the defects of traditional APF, a novel improved APF algorithm named back virtual obstacle setting strategy-APF (BVO-APF) has been proposed in this paper. There are two main advantages of the proposed method. Firstly, by redefining the gravitational function as logarithmic function, the proposed method can make the mobile robot reach the target point when there are obstacles around the target. Secondly, the proposed method can avoid falling into local oscillation for both three-point collinear and semi-closed obstacles. Compare with APF and other improved APF, the feasibility of the algorithm is proved through software simulation and practical application.

Intelligent fault diagnosis method is an important tool for ensuring the stability of the industrial process. However, in the actual industrial process, forming a fault diagnosis model with good performance is difficult because of the complexity of feature extraction and the lack of labeled fault data. Data enhancement on the basis of the original data is important. To address this problem, this study proposes a method called self‐attention embedded generative adversarial network combined with residual network (SAGAN‐ResNet). First, to address the lack of fault data, the data augmentation method consisting of the self‐attention embedded generator and discriminator is adopted. Then, to extract the features for better diagnosis performance, the residual network (ResNet) is introduced based on the augmented training dataset. A comparison of the proposed method with others shows that it has advantages in the case of complex process fault diagnosis with few‐shot industrial data.

Aiming at the problem of object model identification of modern industrial process control systems, a new closed-loop moment parameter identification online method based on the data of normal operation of the running system is proposed. In this method, only one step response data of the system is required, and appropriate convergence factors are introduced into the Laplace formula, the trapezoidal integral method is used to calculate the values of two derivatives of the transfer function, then the four unknown parameters of the second-order model can be solved by fitting the data with the least square method, and the target model can be identified. Finally, the simulation results of building different objects through Matlab show that the identification method has general applicability and good robustness with high recognition, and it is not sensitive to noise signals.