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From Static and Dynamic Perspectives:A Survey on Historical Data Benchmarks of control Performance Monitoring

IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 300-316页

作者： Pengyu Song Jie Wang Chunhui Zhao Biao Huang the State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University IEEE the Department of Chemical and Materials Engineering University of Alberta

In recent decades, control performance monitoring(CPM) has experienced remarkable progress in research and industrial applications. While CPM research has been investigated using various benchmarks, the historical data benchmark(HIS) has garnered the most attention due to its practicality and effectiveness. However, existing CPM reviews usually focus on the theoretical benchmark, and there is a lack of an in-depth review that thoroughly explores HIS-based methods. In this article, a comprehensive overview of HIS-based CPM is provided. First, we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo: static and dynamic properties. The static property portrays time-independent variability in system output, and the dynamic property describes temporal behavior driven by closed-loop feedback. Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two ***, two mainstream solutions for CPM methods are summarized, including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior. Furthermore, this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.

关键词： control performance monitoring (CPM) data-driven method historical data benchmark (HIS) industrial process performance index static and dynamic analysis

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Toward Universal controller: Performance-Aware Self-Optimizing Reinforcement Learning for Discrete-Time Systems With Uncontrollable Factors

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3249-3260页

作者： Zhang, Jianfeng Zhang, Haoran Zhao, Chunhui Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China

The industrial system usually contains not only controllable variables (CVs) but also uncontrollable variables (unCVs), e.g., weather conditions and friction. These unCVs have a direct impact on system control performance. Despite the success of current deep reinforcement learning (DRL) control algorithms, most of them neglect the impact of unCVs, which can cause the deterioration of control performance and instability of the system. To perceive and eliminate the impact of unCVs, a performance-aware self-optimizing universal controller (PASOUC) is designed in this article. The PASOUC aims at integrating the representation of unCVs and controller design to perceive and eliminate the impact of unCVs under different conditions, which goes beyond most existing control methods. Technically, a historical trajectory-inspired control performance perceptron is developed to perceive the impact of unCVs on system control performance under different conditions. Subsequently, a new performance-aware reward is designed to integrate the representation of unCVs and controller design while training the DRL controller. In addition, the domain randomization (DR) training strategy is employed to learn a universal control policy, which can access the approximate optimal trajectory under nonideal conditions. In this way, the impact of unCVs can be eliminated. To handle the low efficiency of the DR training, the policy improvement-policy proximal optimization (PI-PPO) is proposed to enhance the convergence speed of the DR training by performing explicit policy improvement. Finally, illustrative examples are presented to demonstrate the superiority of the proposed method. © 2013 IEEE.

关键词： Deep reinforcement learning

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A blast furnace fault monitoring algorithm with low false alarm rate:Ensemble of greedy dynamic principal component analysis-Gaussian mixture model

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Chinese Journal of Chemical engineering 2023年第5期57卷 151-161页

作者： Xiongzhuo Zhu Dali Gao Chong Yang Chunjie Yang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China

The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace ***, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify *** with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.

关键词： Chemical processes Principal component analysis Gaussian mixture model Process monitoring Ensemble Process control

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An Accurate and Interpretable Framework for Trustworthy Process Monitoring

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2241-2252页

作者： Wang, Hao Wang, Zhiyu Niu, Yunlong Liu, Zhaoran Li, Haozhe Liao, Yilin Huang, Yuxin Liu, Xinggao Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China

Trustworthy process monitoring seeks to build an accurate and interpretable monitoring framework, which is critical for ensuring the safety of energy conversion plant (ECP) that operates under extreme working conditions, such as high pressure and temperature. Contemporary self-attentive models, however, fall short in this domain for two main reasons. First, they rely on stepwise correlations that fail to involve physically meaningful semantics in ECP logs, resulting in suboptimal accuracy and interpretability. Second, attention matrices are frequently cluttered with spurious correlations that obscure physically meaningful ones, further impeding effective interpretation. To overcome these issues, we propose AttentionMixer, a framework aimed at improving both accuracy and interpretability of the existing methods, and establish a trustworthy ECP monitoring framework. Specifically, to tackle the first issue, we employ a spatial adaptive message passing block to capture variatewise correlations. This block is coupled with a temporal adaptive message passing block through a mixing operator, yielding a multifaceted representation of ECP logs accounting for both the stepwise and variatewise correlations. Concurrently, to tackle the second issue, we employ a sparse message passing regularizer to filter out spurious correlations. We validate the efficacy of AttentionMixer using two real-world datasets from the radiation monitoring network for Chinese nuclear power plants. © 2020 IEEE.

关键词： Accident prevention

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Pyramid Dynamic Bayesian Networks for key Performance Indicator Prediction in Long Time-Delay industrial Processes

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 661-671页

作者： Zeng, Lingquan Ge, Zhiqiang Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China

Building dynamic Bayesian networks (DBNs) for time-delay industrial processes has always been tough, since the structure learning of the DBN is a NP-hard problem. In this article, a pyramid DBN (PDBN) framework is proposed to speed up modeling and improve feature learning for industrial processes with large time delays. In the PDBN framework, a sequence of small-sized DBNs are established, each of which is a progressively simpler representation of the previous layer, and the feature information learned by the DBN sequence corresponds to the layers in the pyramid. With information fusion of the feature pyramid and a further feature filtering with hill climbing based on Bayesian information criterion, we can restore the feature information of the time-delay industrial processes. Regression models are built based on the features learned by the PDBN framework for further key performance indicator estimation. Advantages of the method have been effectively validated on two actual industrial cases. With multilevel feature learning and filtering, both modeling speed and prediction accuracy have been greatly improved. The proposed method outperforms other similar works on identifying important features in industrial processes and existing DBN-based soft sensors. © 2023 IEEE.

关键词： Hidden Markov models

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Reduced-Order Observer-Based Resilient control for MASs with Time-Varying Delay Against DoS Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on industrial Cyber-Physical Systems 2024年 2卷 51-59页

作者： Qiu, Xiaojie Meng, Wenchao Xu, Jinming Yang, Qinmin Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China

This paper concentrates on the resilient control issue in multi-agent systems (MASs) with the time-varying delay subjected to denial of service (DoS) attacks. To eliminate the impact of DoS attacks, a novel resilient control framework is constructed for MASs, where a variable following the Bernoulli distribution process is utilized to denote the behaviors of the attacker. Besides, we design a novel reduced-order (RO) observer with internal time-varying delay to estimate the unknown output of the system and compensate for lost data in the channels. Different from the general observers, the designed observer can ensure both the reduced-order observation errors and the full-order ones can converge to zero. Furthermore, an RO observer-based distributed protocol is devised such that the consensus errors of the closed-loop systems asymptotically converge to zero. Simulation comparisons are provided to validate the efficiency and superiority of our approach. © 2023 IEEE.

关键词： Multi agent systems

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Unmanned aerial vehicle orthogonal laser localization by Gaussian mixture model‐based map representation

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IET Cyber-Systems and Robotics 2023年第3期5卷 37-44页

作者： Zeyu Wan Changjian Jiang Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhouChina

Localization is a core problem in mobile robot *** localization and mapping(SLAM)costs much for an unmanned aerial vehicle(UAV).This research aims to design an orthogonal laser scan device for localization and to save computation *** on disturbance analysis,residual influences on sensor state are quantitative,and they are related to uncertainty and *** research applied the residual selection method to a *** feature point detection utilises multi‐scale and Gaussian model fitting techniques to guarantee true *** map is represented by Gaussian Mixture Models(GMM)with lower memory *** orthogonal laser scan device is composed and placed on a UAV for real‐time three‐dimensional localization,whose er-rors are at the centimeter level.

关键词： indoor positioning mobile robots unmanned aerial vehicle

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Feature-Aligned Stacked Autoencoder: A Novel Semisupervised Deep Learning Model for Pattern Classification of industrial Faults

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 592-601页

作者： Zhang, Xinmin Zhang, Hongyi Song, Zhihuan Zhejiang University College of Control Science and Engineering State Key Laboratory Industrial Control Technology Hangzhou310027 China

Autoencoder is a widely used deep learning method, which first extracts features from all data through unsupervised reconstruction, and then fine-tunes the network with labeled data. However, due to the limited number of labeled data samples, the network may lack sufficient generalization ability and is prone to overfitting. This article proposes a new semisupervised deep learning method called feature-aligned stacked autoencoder (FA-SAE). FA-SAE takes advantage of the unlabeled data during the fine-tuning process by aligning the feature of both labeled and unlabeled data. In FA-SAE, a new training loss function is designed by integrating the Sinkhorn distance measure of the difference between the features extracted from labeled and unlabeled data through the neural network into the cross-entropy classification loss. The effectiveness of the proposed FA-SAE is verified through its application to two industrial processes, and the application results demonstrated that the proposed FA-SAE has better generalization ability and higher fault classification accuracy as compared to the state-of-the-art methods. © 2020 IEEE.

关键词： Deep learning

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Matrix Coding Enabled Impact Mitigation against Primary False Data Injection Attacks in Cyber-Physical Microgrids

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IEEE Transactions on Power Systems 2025年第4期40卷 3144-3159页

作者： Liu, Mengxiang Zhang, Xin Zhao, Chengcheng Deng, Ruilong University of Sheffield School of Electrical and Electronic Engineering Sheffield United Kingdom Zhejiang University State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Hangzhou China

The impact mitigation against false data injection attacks (FDIAs) has become a prevailing topic in enhancing the cyber resilience of microgrids. In particular, the primary FDIA (PFDIA) injecting biases into the sensor channel of the primary controller can fake the real physical states and result in devastating control commands to the power conversion device. Nevertheless, existing impact mitigation schemes cannot handle the PFDIA due to the primary control's strict real-time requirement. Therefore, this paper proposes a time- and cost-efficient impact mitigation scheme against the PFDIA by alternately encoding the transmitted measurement with an invertible coding matrix. Specifically, when the PFDIA is detected by unknown input observers (UIOs), two additional half-downsampled UIOs, which only require simple multiplication, addition, and subtraction operations within each control cycle, will be triggered to obtain the residuals under encoded and unencoded data. The complete bias vector can be then reconstructed recursively from these two residuals, and the bias will be removed from the compromised data to eliminate the malicious attack impact. Based on the theoretical analysis of reconstruction performance, the coding matrix is optimised to minimise the system noises' impact on reconstruction accuracy subject to the reconstruction stability and the encoding's hiddenness from the adversary. Finally, extensive experimental studies are conducted to validate the effectiveness, superiority, robustness, and lightweightness of the proposed impact mitigation scheme. © 1969-2012 IEEE.

关键词： Encoding (symbols)

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Augmented industrial Data-Driven Modeling Under the Curse of Dimensionality

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1445-1461页

作者： Xiaoyu Jiang Xiangyin Kong Zhiqiang Ge the State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China the Peng Cheng Laboratory Shenzhen 518000China

The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional *** recent years,the types and vari ables of industrial data have increased significantly,making data driven models more challenging to *** address this prob lem,data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensiona industrial *** paper systematically explores and discusses the necessity,feasibility,and effectiveness of augmented indus trial data-driven modeling in the context of the curse of dimen sionality and virtual big ***,the process of data augmen tation modeling is analyzed,and the concept of data boosting augmentation is *** data boosting augmentation involves designing the reliability weight and actual-virtual weigh functions,and developing a double weighted partial least squares model to optimize the three stages of data generation,data fusion and *** approach significantly improves the inter pretability,effectiveness,and practicality of data augmentation in the industrial ***,the proposed method is verified using practical examples of fault diagnosis systems and virtua measurement systems in the *** results demonstrate the effectiveness of the proposed approach in improving the accu racy and robustness of data-driven models,making them more suitable for real-world industrial applications.

关键词： Index Terms—Curse of dimensionality data augmentation data-driven modeling industrial processes machine learning

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