IEEE Transactions on Industrial Cyber-Physical Systems最新文献

Semi-Supervised Data Distillation for Deep Soft Sensing in Industrial Cyber-Physical Systems 工业信息物理系统中深度软检测的半监督数据蒸馏

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-02-18 DOI: 10.1109/TICPS.2026.3666166

Zhiqiang Ge;Junhua Zheng;Li Sun

{"title":"Semi-Supervised Data Distillation for Deep Soft Sensing in Industrial Cyber-Physical Systems","authors":"Zhiqiang Ge;Junhua Zheng;Li Sun","doi":"10.1109/TICPS.2026.3666166","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3666166","url":null,"abstract":"While the performances of current industrial data-driven soft sensors are mainly improved through the model aspect, little attention has been paid to the data aspect which has been considered as an even more important element from the recent viewpoint of data-centric engineering and artificial intelligence. In this paper, a new exponential weighted moving average data distillation method is proposed for the design of a semi-supervised soft sensor for industrial cyber-physical systems. Through the introduction of a data distillation method, additional unlabeled data samples can be naturally incorporated for semi-supervised soft sensor modeling, without increasing the number of training data samples, thus the computational complexity of the model can be well controlled. Particularly, a new condensed labeled dataset is formulated, which contains information from both original labeled and unlabeled datasets. Another benefit of data distillation lies in its potential solution for the problem of timeline alignment between input and output variables in soft sensor modeling, which has been ignored in most current works. Two industrial case studies are provided for performance evaluation of the developed method, based on which both feasibility and effectiveness are confirmed.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"129-136"},"PeriodicalIF":0.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Verifiable Digital Twin-Aided AI (DTAI) Agent-Based Production System Framework With Causally Consistent Symbiotic Simulation 基于可验证数字双辅助人工智能（DTAI）主体的生产系统框架与因果一致共生仿真

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-02-16 DOI: 10.1109/TICPS.2026.3665499

Bingqing Shen;Yuxin Zeng;Han Yu;Pan Hu;Zixi Han;Hongming Cai

{"title":"A Verifiable Digital Twin-Aided AI (DTAI) Agent-Based Production System Framework With Causally Consistent Symbiotic Simulation","authors":"Bingqing Shen;Yuxin Zeng;Han Yu;Pan Hu;Zixi Han;Hongming Cai","doi":"10.1109/TICPS.2026.3665499","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3665499","url":null,"abstract":"Generative AI enables agent-based smart manufacturing to achieve flexible and reactive decision-making with pre-trained models. However, AI agents remain weak in plan verification and are vulnerable to perception errors caused by dynamic environments and inconsistent Industrial Internet of Things data. These limitations critically undermine the effectiveness of AI agent planning in complex production environments. To address the limitations, this study proposes a digital twin-aided AI (DTAI) agent-based production system, in which digital twins provide a causal data provisioning and simulation layer to support situation awareness and decision validation. A causally consistent symbiotic simulation approach is further introduced to align simulation results with actual production processes while reducing monitoring overhead. Controlled experiments show that the proposed approach achieves a higher rate of optimal scheduling while reducing monitoring overhead with production increase, and lowers computational cost compared with alternative digital twin synchronization designs. A large-scale aerospace manufacturing case study demonstrates that DTAI-based scheduling with consistent symbiotic simulation can largely reduce rescheduling rate by identifying and eliminating most resource conflicts, which further improves multi-objective production efficiency, including equipment usage, makespan, etc. These results indicate that the proposed framework provides a practical and verifiable solution for deploying AI agent-based intelligent manufacturing systems.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"137-149"},"PeriodicalIF":0.0,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Generative DRL Framework for Adaptive Quality Control in Industrial Cyber-Physical Production Systems 工业信息物理生产系统中自适应质量控制的生成式DRL框架

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-02-09 DOI: 10.1109/TICPS.2026.3663105

Pengshuo Wang;Wenjing Xiao;Miaojiang Chen;Yueqian Zhang;Qingnian Li;Jiarong Liang;Ahmed Farouk

{"title":"A Generative DRL Framework for Adaptive Quality Control in Industrial Cyber-Physical Production Systems","authors":"Pengshuo Wang;Wenjing Xiao;Miaojiang Chen;Yueqian Zhang;Qingnian Li;Jiarong Liang;Ahmed Farouk","doi":"10.1109/TICPS.2026.3663105","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3663105","url":null,"abstract":"The manufacturing sector faces escalating pressures to enhance production efficiency, product yield, and energy utilization, making Intelligent Quality Inspection (IQI) a cornerstone of product reliability. To address this need, we propose a data-driven decision-making framework for IQI, grounded in generative artificial intelligence and deep reinforcement learning. This study focuses on the component assembly process, a critical stage in manufacturing. We first develop a generative AI-driven daptive framework that constructs a high-fidelity assembly line. This environment realistically incorporates stochastic factors such as component defective rates, inspection mechanisms, and handling strategies for non-conforming products. To provide a rigorous foundation for algorithmic optimization, we formally model this dynamic ecosystem as a Markov Decision Process (MDP), defining its state space and probabilistic transition rules. We then introduce the integrated Rainbow Deep Q-Network (Rainbow DQN) algorithm to solve the resulting multi-step sequential decision-making problem. Our approach leverages the synergistic advantages of multiple DQN extensions to master dynamic quality control policies. Extensive experimental evaluations, including ablation studies and comparisons against benchmark algorithms such as traditional Monte Carlo methods, confirm the superiority of our proposed framework. The results demonstrate significant advantages in convergence speed, decision-making accuracy, and computational efficiency, underscoring its potential to advance the capabilities of intelligent quality inspection in modern industrial cyber-physical systems.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"119-128"},"PeriodicalIF":0.0,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Multi-Objective Evolutionary Algorithm for Proxy-Based Energy-Aware Scheduling in Aircraft Assembly Line 基于代理的飞机装配线能量感知调度多目标进化算法

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-20 DOI: 10.1109/TICPS.2026.3656106

Yulai Wang;Renzhi Lu

{"title":"A Novel Multi-Objective Evolutionary Algorithm for Proxy-Based Energy-Aware Scheduling in Aircraft Assembly Line","authors":"Yulai Wang;Renzhi Lu","doi":"10.1109/TICPS.2026.3656106","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3656106","url":null,"abstract":"The evolution of aircraft design has resulted in increasingly intricate aircraft manufacturing processes characterized by increased performance requirements and stringent quality standards. Consequently, the aeronautical assembly industry is confronted with substantial new challenges in fulfilling these demands. This work analyzes aircraft final assembly line scheduling including three critical issues: proxy-based energy-aware scheduling, worker assignment, and process planning. Specifically, it addresses an integrated assembly line problem focused on complete assembly tasks, with optimization criteria aimed at minimizing total delay and alleviating workload imbalance along the assembly line. To formalize the practical optimization problem, a multi-objective nonlinear mathematical model is developed. Then, an improved multi-objective genetic algorithm is proposed, which directs the convergence of the Pareto front by incorporating an enhanced hierarchical clustering mechanism that incorporates both fitness and chromosome dimensions. An adaptive factor and an objective function are introduced to increase the convergence speed of the algorithm to align with real production needs. Finally, through independent experiments, the proposed algorithm is rigorously compared to other multi-objective algorithms, demonstrating that the proposed algorithm achieves respective improvements of 4.0% and 8.5% in the inverted generational distance (IGD) metric and 10.6% and 18.1% in the hypervolume (HV) metric, as compared to two state-of-the-art algorithms.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"82-93"},"PeriodicalIF":0.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Finite-Time Event-Triggered Control for Switched Affine Time-Delay Cyber-Physical Systems With Asynchronous Switching 异步切换仿射时延网络物理系统的有限时间事件触发控制

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-15 DOI: 10.1109/TICPS.2026.3654484

Hao Xu;Hongsheng Hu

{"title":"Finite-Time Event-Triggered Control for Switched Affine Time-Delay Cyber-Physical Systems With Asynchronous Switching","authors":"Hao Xu;Hongsheng Hu","doi":"10.1109/TICPS.2026.3654484","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3654484","url":null,"abstract":"For industrial cyber-physical systems (CPSs), which integrate sensors, actuators, computational units, and communication networks to monitor and control critical industrial processes, achieving high efficiency, safety, and real-time performance is of utmost importance. This paper investigates finite-time event-triggered control for switched affine time-delay CPSs under average dwell-time constraints, aiming to resolve controller-system mismatches induced by switching during trigger intervals and to allocate communication resources efficiently. Firstly, an event-triggered mechanism (ETM) incorporating multiple parameter matrices is developed. Unlike conventional designs, the proposed framework accommodates subsystem switching within inter-trigger intervals, ensuring stability despite plant-controller asynchrony. Under the ETM, the co-design of a delay-compensating switching signal and an adaptive state feedback controller through parameter-dependent matrix formulations, are presented. Furthermore, leveraging Lyapunov-Krasovskii functional theory, sufficient stability criteria are established through linear matrix inequalities (LMIs), ensuring finite-time stability (FTS), finite-time boundedness (FTB), and predefined disturbance attenuation levels under average dwell-time restrictions. The Zeno-free execution of triggering sequences are rigorously verified. Experimental results on a DC-DC boost converter verify the correctness of the proposed method, demonstrating that the considered system achieves finite-time convergence even in the presence of affine nonlinearities and external disturbances.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"94-105"},"PeriodicalIF":0.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Cybersecurity in Wind Turbines Against Sensor Attacks Through Adaptive Kalman Filtering 利用自适应卡尔曼滤波增强风力发电机网络安全，抵御传感器攻击

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-06 DOI: 10.1109/TICPS.2026.3651224

Evi Elisa Ambarita;Anniken Karlsen;Francesco Scibilia;Agus Hasan

{"title":"Enhancing Cybersecurity in Wind Turbines Against Sensor Attacks Through Adaptive Kalman Filtering","authors":"Evi Elisa Ambarita;Anniken Karlsen;Francesco Scibilia;Agus Hasan","doi":"10.1109/TICPS.2026.3651224","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3651224","url":null,"abstract":"As modern power systems increasingly integrate wind energy, the cybersecurity of wind turbine control systems becomes a critical concern. In particular, sensor attacks can compromise operational safety and lead to significant performance degradation. This paper introduces an innovative approach aimed at enhancing cybersecurity of wind turbines against such attacks. The proposed approach leverages an adaptive Kalman filter, augmented with a forgetting factor to dynamically adjust to changing conditions. The mathematical analysis of the adaptive Kalman filter's exponential stability is rigorously examined, under standard observability and controllability conditions. Through numerical simulations in two different scenarios, the effectiveness of the proposed approach is thoroughly evaluated. The results show that the adaptive Kalman filter achieves notable precision in estimating the actual magnitude of the attacks, even in the presence of noise. This resilience demonstrates the robustness of the proposed methodology in safeguarding wind turbine cybersecurity in dynamic and challenging environments.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"62-70"},"PeriodicalIF":0.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resource Allocation Using Reinforcement Learning in Industrial Cyber-Physical Systems 在工业信息物理系统中使用强化学习的资源分配

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-03-30 DOI: 10.1109/TICPS.2026.3678219

Jiaming Pei;Minxi Feng;Anwer Al-Dulaimi

引用次数: 0

Ameliorated Cyber Security for Power Electronic Converters Serving Asynchronous Hydro Generating Systems 异步水力发电系统电力电子变流器的改进网络安全

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-03-21 DOI: 10.1109/TICPS.2026.3695853

Rupesh Kumari

{"title":"Ameliorated Cyber Security for Power Electronic Converters Serving Asynchronous Hydro Generating Systems","authors":"Rupesh Kumari","doi":"10.1109/TICPS.2026.3695853","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3695853","url":null,"abstract":"In recent years, the threat of cyber-attacks on industrial process controllers and power electronics converters (PECs) has highlighted the need for enhanced research into the control systems of hydropower plants. These systems have garnered significant attention due to their critical role in energy infrastructure. The IEC Standard 62433, Industrial Automation and Control System Security, provides key recommendations to address these concerns. With PECs now being network-capable, they hold significant potential to integrate into the Internet of Energy (IoE). However, this connectivity also increases their vulnerability to cybersecurity risks. This study proposes a parallel controller strategy (PCS) to protect PECs in hydropower plants from cyber-attacks. The PCS combines a digital and an analog controller to manage large variable hydropower systems effectively. A 250 MW hydroelectric generating unit serves as the basis for this research. The simulation results are analyzed to assess the system's performance against various cyber-attacks across different operating modes of the machine. The simulation and experimental results demonstrate the efficacy of the proposed technique.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"346-355"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Secure State Estimation Against Sparse Sensor Attacks: An Adaptive Gain Modulation Approach 针对稀疏传感器攻击的分布式安全状态估计：一种自适应增益调制方法

IF 7.4

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-06-08 DOI: 10.1109/TICPS.2026.3701323

Xuqiang Lei;Guanghui Wen;Dan Zhao;Xinghuo Yu;Guanrong Chen

引用次数: 0

A Sensing-Network-Control Co-Design Framework for TSN-Enabled Industrial Cyber-Physical Systems 面向tsn的工业信息物理系统的传感-网络-控制协同设计框架

IF 7.4

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-06-29 DOI: 10.1109/TICPS.2026.3708069

Xiaolin Wang;Yuhang Wei;Yao Li;Jinglong Zhang;Xinping Guan

{"title":"A Sensing-Network-Control Co-Design Framework for TSN-Enabled Industrial Cyber-Physical Systems","authors":"Xiaolin Wang;Yuhang Wei;Yao Li;Jinglong Zhang;Xinping Guan","doi":"10.1109/TICPS.2026.3708069","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3708069","url":null,"abstract":"In industrial cyber-physical systems, multiple control subsystems rely on large-scale sensors and industrial networks for collaborative closed-loop control. Although increasing the number of sensors can improve sensing quality and control accuracy, it also increases network load and changes network delay, which further affects closed-loop performance. Therefore, sensor deployment, network resource allocation, and controller design are tightly coupled across layers. To address this issue, this paper proposes a sensing–network–control co-design framework for multi-subsystem collaborative control. Leveraging the deterministic transmission capability of Time-Sensitive Networking (TSN), the proposed framework jointly models the relationships among the number of sensors, bandwidth allocation ratios, network delay, and control cost, and formulates a unified optimization problem integrating sensing, network, and control costs. Based on this framework, an optimal distributed <inline-formula><tex-math>$H_{2}$</tex-math></inline-formula> controller with zero-order hold (ZOH) and a two-stage constraint-aware scheduling algorithm (TCSA) are developed to jointly optimize sensor deployment and network resource allocation. Simulation results validate the effectiveness of the proposed framework and the TCSA algorithm.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"731-740"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148747295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0