Against the backdrop of Industry 4.0, complex industrial processes face heightened risks of performance degradation and safety hazards due to increasing scale and integration. To address this, we propose a novel cloud-edge-device collaborative framework for hierarchical performance monitoring and fault diagnosis. Our approach explicitly targets multi-performance coupling and cross-domain coordination in complex systems. Aligned with the architecture of manufacturing systems, a cloud-edge-device collaborative framework is designed deeply integrating models for safety control, quality monitoring, and energy consumption prediction. The architecture strategically distributes tasks: (1) At the device layer, real-time control-loop safety is considered and the safety-related data processing and alignment are realized; (2) The edge layer deploys a dual-attention minimal gated unit network coupled with a quality-driven autoencoder, enabling temporal and nonlinear feature extraction for quality-centric fault diagnosis within sub-processes; (3) The cloud layer integrates self-attention minimal gated units with a broad learning system for multi-perspective energy efficiency prediction and holistic evaluation. Finally, a hot strip mill process prototype system is designed and developed to validate the effectiveness and engineering value of the proposed framework through practical case studies.

As large-scale industrial processes evolve toward greater complexity, the increasing interdependence of networked and dynamic process data has a critical impact on product quality, creating significant challenges for quality-related fault diagnosis. Causal graphs (CGs) are effective in modeling structural relationships among nodes in large-scale industrial processes. However, traditional causal discovery methods are limited in their ability to represent hierarchical and dynamic causal structures with spatiotemporal features. To overcome these limitations, a dynamic causal entropy (DCE)-spatiotemporal convolutional network is designed in this article. First, the proposed DCE method enables the construction of hierarchical dynamic CGs that accurately represent dynamic interactions among process variables, effectively mitigating confounding factors and enhancing interpretability. Second, a 3-D squeeze-and-excitation (SE) convolutional neural network is designed to adaptively recalibrate channel-wise information and deeply analyze the spatiotemporal characteristics embedded in the hierarchical dynamic CGs. Furthermore, a local-global quality-related fault detection approach is introduced, along with a novel causal anomaly vector that facilitates precise recognition of fault root causes across multiple hierarchical levels. Finally, the effectiveness and practical advantages of the proposed method are thoroughly demonstrated using both numerical simulations and real-world data from a hot strip mill process (HSMP), achieving a fault detection accuracy of 95.78%.

Single-master multislave (SMMS) teleoperation systems can perform multiple tasks remotely in a shorter time, cover large-scale areas, and adapt more easily to single-point failures, thereby effectively encompassing a broader range of applications. As the number of slave manipulators sharing a communication network increases, the limitation of communication bandwidth becomes critical. To alleviate bandwidth usage, the try-once-discard (TOD) scheduling protocol and event-triggered mechanisms are often employed separately. In this article, we combine both strategies to optimize network bandwidth and energy consumption for SMMS teleoperation systems. Specifically, we propose event-triggered control and communication schemes for a class of SMMS teleoperation systems using the TOD scheduling protocol. Considering dynamic uncertainties, the unavailability of relative velocities, and time-varying delays, we develop adaptive controllers with virtual observers based on event-triggered schemes to achieve master-slave synchronization. Stability criteria for the SMMS teleoperation systems under these event-triggered control and communication schemes are established, demonstrating that Zeno behavior is excluded. Finally, experiments are conducted to validate the effectiveness of the proposed algorithms.

Modern industrial plants generally demonstrate the characteristics of large scale, long process, and multiunit collaborative operation, which makes the spatio-temporal distribution an inherent nature, and the quality stability is usually hard to be guaranteed. A quality-related spatio-temporal information analytics based multiunit synergetic monitoring framework is presented in this paper. In this framework, the spatio-temporal properties are analyzed from the unit level and the process level, respectively. Firstly, for each operation unit, the quality supervised spatio-temporal support region is constructed with a concurrent feature extraction strategy. In this strategy, temporal dynamic features are extracted by a long short term memory (LSTM) network with attention mechanism. Concurrently, the spatial feature is extracted with the mutual information-kernel principal component analysis approach. Secondly, for the plant-wide process, a third order multiunit-spatio-temporal feature tensor is constructed for feature fusions. Via tensor decomposition, the interconnected associations among units and the quality inheritance along the process are explored, and the original feature space is decomposed into several subspaces. Finally, a multiunit synergetic monitoring model is developed over subspaces and the comprehensive monitoring results are given by Bayesian fusion. Reasonable interpretations can be provided in the monitoring results. The effectiveness of the proposed framework is verified on a real hot strip mill process.Note to Practitioners—This paper intends to provide a spatio-temporal information analytics and fusion framework for multiunit processes and to develop a quality-related process monitoring method for industrial plants. Different from the existing works, the monitoring model built in this paper is based on the multiunit-spatio-temporal sensitive information, which is extracted by a concurrent strategy and fused by the tensor model. In addition, the quality inheritance among multiunits is considered in this framework and the monitoring results in each subspace can provide helpful instructions for the field technicians. In detail, the fault-relevant unit can be located by the relatively independent subspace monitoring, and the quality-related anomaly propagation tendency can be indicated by the strong associative subspace monitoring.

The networked structure of sensors emerges in large-scale industrial processes. Causal graphs can reveal the underlying mechanisms. However, due to the constraints of material and information flows, industrial process data exhibit complex spatio–temporal characteristics. Traditional causal discovery results include redundant information and the spatio–temporal features are not sufficiently mined, affecting the accuracy of fault diagnosis. To address the above problems, an integrated distributed fault diagnosis framework is proposed. First, a new method combining mechanism knowledge and correlation is proposed to construct a spatio–temporal causal graph, which highlight spatio–temporal causal information. Second, an embedded time convolutional network-based autoencoder is designed to extract spatio–temporal features simultaneously. Then, the local-global fault detection scheme is performed. On this basis, a new anomaly status information matrix is designed by decoder and spatial features to achieve root cause recognition. Finally, the effectiveness of the proposed method is validated using actual data from the hot strip mill process, achieving a fault detection accuracy of 98.3$\%$.

Due to the interconnected characteristics between subsystems and the strong correlation within subsystems, the monitoring of plant-wide processes has become a challenging problem, especially for tandem plant-wide processes that exist in various industrial fields, such as petrochemicals, metallurgy, and sewage treatment. In this article, a novel spatio-temporal monitoring method is proposed for the hot strip mill (HSM) process, a typical tandem industrial process. First, the plant-wide process is divided into different subblocks based on the tandem structure. Then, a distributed conditional variational recurrent autoencoder-based process monitoring method is proposed to build the local latent variable model of each subsystem using relevant dynamic features extracted from the previous subsystem. The latent distributions and reconstructed errors are used to design local monitoring statistics for local process monitoring. A global monitoring statistic is established by deep support vector data description to monitor the whole process. Finally, the effectiveness and superiority of the proposed method are demonstrated by a HSM process case, which shows better monitoring performance compared to the existing methods.

In industrial processes, operating performance assessment is of great practical significance for guiding the production adjustment for operators. From the perspective of classification, operating performance assessment is considered as a multi-class classification problem. As a well-known one-class classifier, support vector data description (SVDD) are oriented to vector data and cannot deal with tensor data directly. Moreover, SVDD gives the target data set a spherically shaped description, which is a binary output. However, practical industrial data of different operating performance grade may have overlapping region, which is a knotty problem for classification. To handle above issues, a distributed operating performance assessment method based on probabilistic support tensor data description (PSTDD) is proposed in this work. First, the plant-wide process variables are selected and divided into several blocks. Then, a PSTDD model is developed in each block. Based on the assessment results of different blocks, a global assessment index is designed. If the process is running at non-optimal condition, the root cause are traced by variable contributions. Experimental results on a real hot strip mill process (HSMP) illustrate the effectiveness of the proposed method comparing to the traditional distributed SVDD.

An imbalanced number of faulty and normal samples make the traditional supervised classification methods difficult to ensure their classification performance. Accordingly, semisupervised learning methods have recently become hotspots both in academic research and practical application domains. Different from previous schemes, this paper dedicates on the correlations, common features, and specific features among quality-related coupling faults in manufacturing industries. The main innovations are as follows: first, it is the first time to develop a robust semisupervised classification framework for quality-related coupling faults, which integrates semisupervised multitask feature selection and manifold learning; second, manifold structures and local discriminant information of unlabeled and limited labeled faulty samples are sufficiently explored to improve the classification performance; and third, correlations among quality-related coupling faults are accurately captured, which are crucial for understanding the uniqueness and relationships of them at the feature level. The proposed method is finally validated in a representative manufacturing industry, i.e., hot strip mill process, where detailed simulation processes are presented and better classification performance is shown compared with the existing approaches.

In actual production processes, the occurrence probability of multiple faults is much higher than that of a single fault, which will affect the process industry operating performance and final products quality. This paper is concerned with industrial practices and theoretical approaches for detection and location of key performance indicator (KPI) related multiple faults in process industries. First, a new KPI-related multiple fault monitoring scheme is addressed from the subprocess level based on the developed correlation-based canonical variable analysis model. Then, Bayesian fusion is implemented to form the final monitoring decisions from the plantwide level. After that, a tensor subspace analysis-based discriminant analysis method is proposed for locating the root causes, which will help field engineers to take correction actions and recover the process operations. Finally, the application to a typical industry process, i.e., hot strip mill process, is given to demonstrate the performance and effectiveness of the proposed methods with real industrial data.

This paper proposes a common and individual (CnI) feature extraction-based process monitoring (PM) method for tracking the operating performance and product quality of processes with multiple operating modes. Different from traditional methods that separately develop PM models concerning only the individual feature of each mode data, the new method seeks to build the PM model simultaneously from all mode data, including to acquire the common subspace that captures the common feature behind different modes, and the individual subspace that reflects the unique feature of each mode. The newly proposed framework is achieved using the conventional principal component analysis (PCA) and partial least squares (PLS) based methods. The resulting CnI-PCA-based operating performance monitoring method and CnI-PLS-based product quality monitoring method are applied to the typical multimode finishing mill process (FMP) where common configuration for all steel products and individual setting for each steel are existing. Finally, the practical application result shows that the proposed method can be preferable to detect and identify different faults in the multimode FMP.

VFH*could not make optimal choice of direction in the narrow stability region. An improved method called IVFH*enlarges certain value (CV), raise more grids, add to relative threshold and introduce the concept of reactive deformation links to improve the original algorithm. IVFH*use Newtonian Physics and Hooker's Law to update the position of the nodes and deform the links in response to the motion of the obstacles, for avoiding dynamic obstacles in dynamic environment.

Hot strip mill is a typical process with high accuracy and high speed. The effective approach to enhance the products competitiveness is to improve the quality and quantity by means of advanced control strategy. A multivariable system comprised of gauge and mass flow of hot strip mill is analyzed in the article. A state-space model about the gauge and mass flow for one stand and inter-stand in a hot strip mill is set up as well. New steady and coordinated control approach on the basis of LQG and H∞of hot strip mill is proposed and the robust characteristics such as perfect robust stabilization, strong disturbance attenuation and coordination properties are better in the latter. Simulation results show the validity of analysis, design and control.