In modern industrial processes, soft sensors have played an important role for effective process control, optimization, and monitoring. Feature representation is one of the core factors to construct accurate soft sensors. Recently, deep learning techniques have been developed for high-level abstract feature extraction in pattern recognition areas, which also have great potential for soft sensing applications. Hence, deep stacked autoencoder (SAE) is introduced for soft sensor in this paper. As for output prediction purpose, traditional deep learning algorithms cannot extract high-level output-related features. Thus, a novel variable-wise weighted stacked autoencoder (VW-SAE) is proposed for hierarchical output-related feature representation layer by layer. By correlation analysis with the output variable, important variables are identified from other ones in the input layer of each autoencoder. The variables are assigned with different weights accordingly. Then, variable-wise weighted autoencoders are designed and stacked to form deep networks. An industrial application shows that the proposed VW-SAE can give better prediction performance than the traditional multilayer neural networks and SAE.

Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This paper attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs, hot- and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: Statistical, spectral, model-based and machine learning. These literatures are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.

Industrial process data are naturally complex time series with high nonlinearities and dynamics. To model nonlinear dynamic processes, a long short-term memory (LSTM) network is very suitable for soft sensor model development. However, the original LSTM does not consider variable and sample relevance for quality prediction. In order to overcome this problem, a spatiotemporal attention-based LSTM network is proposed for soft sensor modeling, which can, not only identify important input variables that are related to the quality variable at each time step, but also adaptively discover quality-related hidden states across all time steps. By taking the spatiotemporal quality-relevant interactions into consideration, the prediction performance can be improved for the soft sensor model. The effectiveness and flexibility of the proposed model is demonstrated on an industrial hydrocracking process to predict the initial boiling points of heavy naphtha and aviation kerosene.

In this paper, we study the consensus problem for a class of linear multiagent systems, where the communication networks are directed. First, a dynamic event-triggering mechanism is introduced, including some existing static event-triggering mechanisms as its special cases. Second, based on the dynamic event-triggering mechanism, a distributed control protocol is developed, which ensures that all agents can reach consensus with an exponential convergence rate. Third, it is shown that, with the dynamic event-triggering mechanism, the minimum interevent time between any two consecutive triggering instants can be prolonged and no agent exhibits Zeno behavior. Finally, an algorithm is provided to avoid continuous communication when the dynamic event-triggering mechanism is implemented. The effectiveness of the results is confirmed through a numerical example.

Deep-neural network-based fault diagnosis methods have been widely used according to the state of the art. However, a few of them consider the prior knowledge of the system of interest, which is beneficial for fault diagnosis. To this end, a new fault diagnosis method based on the graph convolutional network (GCN) using a hybrid of the available measurement and the prior knowledge is proposed. Specifically, this method first uses the structural analysis (SA) method to prediagnose the fault and then converts the prediagnosis results into the association graph. Then, the graph and measurements are sent into the GCN model, in which a weight coefficient is introduced to adjust the influence of measurements and the prior knowledge. In this method, the graph structure of GCN is used as a joint point to connect SA based on the model and GCN based on data. In order to verify the effectiveness of the proposed method, an experiment is carried out. The results show that the proposed method, which combines the advantages of both SA and GCN, has better diagnosis results than the existing methods based on common evaluation indicators.