Industrial process data are usually time-series data collected by sensors, which have the characteristics of high nonlinearity, dynamics, and noises. Many existing soft sensor modeling methods usually focus on dominant variables and auxiliary variables at a single time point while ignoring the timing characteristics of industrial process data. Meanwhile, the soft-sensing methods considering timing characteristics based on the deep learning are usually faced with gradient vanishing and the difficulty in parallel computing. Therefore, a novel Gated Convolutional neural network-based Transformer (GCT) is proposed for dynamic soft sensor modeling of industrial processes. The GCT encodes short-term patterns of the time series data and filters important features adaptively through an improved gated convolutional neural network (CNN). Then, the multihead attention mechanism is applied to modeling the correlation between any two moments. Finally, the prediction results are obtained through a linear neural network layer with the highway connection. In this article, the experiments in the dynamic soft sensor modeling of polypropylene and purified terephthalic acid industrial processes show that the proposed method achieves state-of-the-art comparing with the back propagation neural network, the extreme learning machine, the long short-term memory (LSTM) and the LSTM based on the CNN.

The modular multilevel converter (MMC) is the main part of MMC-based high-voltage direct current (HVDC) system. The MMC bridge arm inductance fault and the submodule IGBT fault have the greatest influence on the transmission quality of transmission systems. Therefore, this article proposes a novel fault diagnosis method based on short-time wavelet entropy integrating the long short-term memory network (LSTM) and the support vector machine (SVM). The proposed short-time wavelet entropy calculation method is used to extract the fault information. First, the optimal short-term wavelet packet calculation period is determined. Moreover, the improved LSTM topology can process the wavelet entropy fault information in the time dimension. Then, the output of the LSTM is set as the input of the SVM to obtain the fault diagnosis result based on the adaptive classification. Finally, through the MMC fault diagnosis experiment of the double-ended MMC-HVDC transmission system, the effectiveness of the proposed method is verified. Compared with the traditional fault diagnosis method, the proposed method has better robustness, adaptability, and accuracy, which can greatly reduce the number of electrical signal samples and realize the fault diagnosis of multiple fault types by collecting a single signal.

The existing fault diagnosis methods can achieve good results when various status fault data are available. However, the construction of the diagnosis model is often unachievable in the actual application because only normal data are available, which is actually a few-shot fault diagnosis problem. Therefore, a novel intelligent few-shot fault diagnosis method of rotating machinery based on the convolutional neural network (CNN) using virtual samples generated by the mechanism character generative model (MCGM) integrating the generative adversarial network (GAN) is proposed. The distribution pattern of common parameters that reflect the fault category is learned using the GAN and source domain fault data. Then, the normal state data of the target domain is combined with the distribution common parameters to generate virtual samples in target domain based on the MCGM. Moreover, the fault diagnosis model is trained by virtual samples based on the CNN. Finally, the proposed fault diagnosis method is validated using the laboratory bearing data, the industrial data and the public data of the rotating machinery, respectively. The results show that the proposed method achieves an average accuracy of 93.38% in the diagnostic task, exhibiting at least 4.56% better performance than other comparison methods.