IET Cyber-Physical Systems: Theory and Applications最新文献

Blockchain-Enabled Secure IoT-Based Smart Grid for Distributed Energy Resource Systems Using Federated Learning 使用联邦学习的分布式能源系统基于区块链的安全物联网智能电网

IF 1.9

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-10-07 DOI: 10.1049/cps2.70068

Gebrekiros Gebreyesus Gebremariam, Abrha Ftsum Berhe, Letebrhan Gebrehiwot Abrha

{"title":"Blockchain-Enabled Secure IoT-Based Smart Grid for Distributed Energy Resource Systems Using Federated Learning","authors":"Gebrekiros Gebreyesus Gebremariam, Abrha Ftsum Berhe, Letebrhan Gebrehiwot Abrha","doi":"10.1049/cps2.70068","DOIUrl":"https://doi.org/10.1049/cps2.70068","url":null,"abstract":"Distributed energy resources (DERs) are increasingly proliferating worldwide, driven by their benefits in promoting energy sustainability, efficiency, and resiliency. Coordinated approaches are crucial for aggregating diverse DERs across large areas, yet the increasing reliance on information technology exposes power systems to cyberattacks. Integrating DERs into the power system increases exposure to cyber threats due to their distributed nature and often insufficient security measures. A layered, defence-in-depth strategy is crucial for protecting these resources. It should incorporate robust technical safeguards and continuous monitoring to ensure secure and reliable operation of DERs in modern power grids. Unfortunately, the increased information flow will increase the potential attack surface and introduce new vulnerabilities. In this paper, we proposed blockchain-enabled secure IoT-based smart grid for distributed energy resource systems using federated learning. The simulation results clearly demonstrate the robustness and effectiveness of the proposed blockchain-enabled secure smart grid for distributed energy resource systems using federated learning in detecting and classifying cyberattacks across multiple benchmark datasets. The model achieves consistently high detection accuracy, reaching 99.12% on CICIDS2017, 98.75% on UNSW-NB15, and 99.3% on CICDDoS2019, indicating strong generalisation capability across diverse network traffic patterns and attack scenarios. The results confirm that the proposed system achieves enhanced scalability, reduced computational complexity, faster processing, and improved real-time responsiveness for secure smart grid and distributed energy resource management applications.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"11 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149145935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Cyber-Physical System Security Through Privacy-Preserving Darknet Traffic Classification With XGBoost 利用XGBoost保护隐私的暗网流量分类增强网络物理系统的安全性

IF 1.9

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-10-06 DOI: 10.1049/cps2.70066

Ankita Kumari, Upinder Kaur, Sheifali Gupta, Jaibir Singh, Ajay Kumar, Suman Rani, Belayneh Matebie Taye, Nitin Kumar, Vivek John, Kaushal kumar, Ateeq Ur Rehman

{"title":"Enhancing Cyber-Physical System Security Through Privacy-Preserving Darknet Traffic Classification With XGBoost","authors":"Ankita Kumari, Upinder Kaur, Sheifali Gupta, Jaibir Singh, Ajay Kumar, Suman Rani, Belayneh Matebie Taye, Nitin Kumar, Vivek John, Kaushal kumar, Ateeq Ur Rehman","doi":"10.1049/cps2.70066","DOIUrl":"https://doi.org/10.1049/cps2.70066","url":null,"abstract":"The growing use of anonymous communication networks, such as the Darknet, creates significant difficulties in identifying malicious operations hiding in encrypted data communications. Traditional machine-learning models often struggle to accurately classify such traffic due to feature redundancy, class imbalance and the complexity of traffic patterns. This study provides a framework for classifying Darknet traffic using the CIC-Darknet2020 dataset with a boosted eXtreme Gradient Boosting (XGBoost) algorithm. The proposed method represents network traffic characteristics by incorporating a structured feature-selection mechanism, dimensionless feature weighting and synthetic minority over-sampling technique (SMOTE)-based class balancing to refine them. Additionally, hyperparameter optimisation is performed to improve the model's performance and efficiency. Compared with conventional classifiers such as Support Vector Machines (SVM), Decision Tree (DT) and Random Forest (RF), the proposed approach has achieved better performance, with an accuracy of 96.25%, a precision of 94.3%, a recall of 90.8% and an F1 score of 92.85%. The results show that the model not only improves classification accuracy but also provides better generalisation and robustness in detecting complex Darknet traffic patterns. Furthermore, the framework ensures privacy because it uses only flow-level features and therefore does not need to inspect packet payloads. These results highlight the effectiveness of the proposed approach for real-world cybersecurity applications involving encrypted network environments.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"11 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149145944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Q-GALNet With MemAx: A Quantum-Inspired Deep Framework for Scalable Billing Anomaly and Financial Fraud Detection Q-GALNet与MemAx：一个量子启发的可扩展计费异常和金融欺诈检测的深度框架

IF 1.9

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-09-24 DOI: 10.1049/cps2.70067

Prabhu Rengaramanujam, SatheeshKumar Palanisamy, S. Sankar Ganesh, Srikanth Naidu Chandrala, Karthikeyan Ganesan, Jeevitha Kandasamy, Sachin Shrestha

{"title":"Q-GALNet With MemAx: A Quantum-Inspired Deep Framework for Scalable Billing Anomaly and Financial Fraud Detection","authors":"Prabhu Rengaramanujam, SatheeshKumar Palanisamy, S. Sankar Ganesh, Srikanth Naidu Chandrala, Karthikeyan Ganesan, Jeevitha Kandasamy, Sachin Shrestha","doi":"10.1049/cps2.70067","DOIUrl":"https://doi.org/10.1049/cps2.70067","url":null,"abstract":"Billing anomaly detectors need to be both credible and scalable such that they facilitate revenue loss avoidance in the banking sector. In this paper, a multi-level fraud detection model is developed in the form of quantum gated adaptive learning network (Q-GALNet) with memory-Addax (MemAx) feature optimisation algorithm. Proposed Q-GALNet applies a quantum-inspired attention mechanism for better feature representation and noise rejection in transactional complex data. The proposed MemAx module offers a dynamic memory-based running selection process to allow for the retention of useful characteristics and some redundant or useless features, thus improving computational efficiency and model generalisation. We instantiate the proposed framework with three benchmark financial datasets, namely PaySim, credit card fraud and IEEE-CIS. Experiments demonstrate that it behaves reliably and performs better than state of the art. The results shows AUC-ROC, AUC-PR, F1-score and recall of 99.60, 98.98, 98.82 and 98.85 respectively. The framework dramatically reduces training time, testing latency, memory usages and total computation cost by 28.8, 37.5, 33.3 and 37.5 respectively These results suggest that the potential hybrid architecture discussed in this work provides a practical and scalable model for real-time detection of billing anomalies and financial fraud across both existing and emerging digital transaction systems.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"11 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structural and Temporal Analysis of Real-Time Techniques to Improve Understanding and Operation of Intertwined Cyber–Physical System Architectures 实时技术的结构和时间分析，以提高相互交织的信息物理系统架构的理解和操作

IF 1.9

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-08-07 DOI: 10.1049/cps2.70061

Shamina Hossain-McKenzie, Emily Payne, Shining Sun, Logan Blakely, Georgios Fragkos, Chris Goes, Nicholas Jacobs, Astrid Layton, Katherine Davis

{"title":"Structural and Temporal Analysis of Real-Time Techniques to Improve Understanding and Operation of Intertwined Cyber–Physical System Architectures","authors":"Shamina Hossain-McKenzie, Emily Payne, Shining Sun, Logan Blakely, Georgios Fragkos, Chris Goes, Nicholas Jacobs, Astrid Layton, Katherine Davis","doi":"10.1049/cps2.70061","DOIUrl":"https://doi.org/10.1049/cps2.70061","url":null,"abstract":"Critical infrastructure systems such as the electric grid are increasingly cyber–physical; however, despite their inherent cyber–physical characteristics, the physical process system and communication/control network system are traditionally analysed in silos. As these systems become more cyber–physical, it is crucial that models and methods are available to assess the cyber–physical system (CPS) interdependencies, characteristics and event propagation for improved planning, operation and response. Thus, we propose an integrated structural and temporal CPS interdependency analysis framework that provides insight into the CPS function during normal operation as well as disturbances. This structural and temporal analysis of real-time techniques (START) framework is uniquely designed for assessing CPSs by accounting for the challenges of analysing cyber and physical data streams together due to data availability, data type and time scale differences. By leveraging both structural (e.g., graph analysis) and temporal (e.g., data analytics) techniques, different CPS behaviours and configurations can be accounted for. This paper presents the START framework, a subset of techniques applied to exemplar transmission and microgrid systems and analysis of resulting structural and temporal CPS interdependency insights.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"11 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unpacking Big Data Analytics: Mediating Effects on Manufacturing Performance in a Developing Economy 大数据分析：发展中经济体中制造业绩效的中介效应

IF 1.9

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-07-08 DOI: 10.1049/cps2.70060

Mohammad Rakibul Islam Bhuiyan, Al- Amin, M. Helal Uddin Ahmed, Abu Reza Md Towfiqul Islam

{"title":"Unpacking Big Data Analytics: Mediating Effects on Manufacturing Performance in a Developing Economy","authors":"Mohammad Rakibul Islam Bhuiyan, Al- Amin, M. Helal Uddin Ahmed, Abu Reza Md Towfiqul Islam","doi":"10.1049/cps2.70060","DOIUrl":"https://doi.org/10.1049/cps2.70060","url":null,"abstract":"The manufacturing industry increasingly adopts big data analytics (BDA) to drive innovation, reduce production costs, enhance revenue and sustain competitiveness. However, limited research in developing economies, particularly Bangladesh, has investigated the mediating effects of BDA on manufacturing performance. This study addresses this gap by empirically investigating how information quality (IQ) and innovative capability (IC) affect the link between BDA and industry performance. A total of 336 valid responses were collected from 500 distributed surveys (response rate: 67.2%) and analysed using SmartPLS and SPSS. The results reveal that BDA significantly enhances manufacturing performance through mediating pathways via IQ and IC. The R2 values for information quality (0.665), industry performance (0.642) and innovative capability (0.837) indicate strong model predictability. This study is the first to simultaneously explore the mediated dynamics of BDA within Bangladesh's manufacturing sector. The findings highlight key organisational implications, including improved profitability through cost reduction, better employee management and optimised product and service design. Technological implications suggest leveraging artificial intelligence and automated supply chain management systems to strengthen data-informed decision-making and reduce operational hazards.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"11 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decentralised Reinforcement Learning for Dynamic Cyberattack Response in Microgrid Networks 微电网动态网络攻击响应的分散强化学习

IF 1.9

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-06-26 DOI: 10.1049/cps2.70058

Suman Rath, Vivek Kumar Singh

{"title":"Decentralised Reinforcement Learning for Dynamic Cyberattack Response in Microgrid Networks","authors":"Suman Rath, Vivek Kumar Singh","doi":"10.1049/cps2.70058","DOIUrl":"https://doi.org/10.1049/cps2.70058","url":null,"abstract":"Microgrids rely on communication networks for reliable operation, which makes them inherently vulnerable to cyberattacks. Such attacks can destabilise system dynamics and drive states away from their nominal operating trajectories. Although several physics-informed and machine learning-based strategies have been developed to counter these threats, the rapidly evolving cyber landscape enables adversaries to bypass static defences or rules-based mitigation approaches. This paper proposes a dynamic, online-trained and fully decentralised reinforcement learning (RL)-based cyberattack response framework to protect microgrids from evolving cyberattacks. The proposed framework deploys multiple deep Q-networks (DQNs), each associated with a distributed energy resource (DER), to enable localised and adaptive attack mitigation. In this framework, each DQN processes local voltage and frequency measurements—combined with intrusion detection system (IDS) alerts—as observations and rewards to guide decision-making. Extensive simulation studies demonstrate the robustness of the proposed framework under diverse attack scenarios and varying IDS-induced detection delays. Comparative analysis highlights its superiority over existing static or preexisting rules-based mitigation approaches. Finally, we present an analysis that shows the framework's scalability to real-life microgrids with more interacting agents.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"11 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis and Optimisation of Multishaper Multidomain Time-Sensitive Networks: State-of-the-Art and Open Challenges 多形状多域时间敏感网络的分析和优化：最新的和开放的挑战

IF 1.9

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-06-19 DOI: 10.1049/cps2.70057

Voica Gavriluţ, Paul Pop

{"title":"Analysis and Optimisation of Multishaper Multidomain Time-Sensitive Networks: State-of-the-Art and Open Challenges","authors":"Voica Gavriluţ, Paul Pop","doi":"10.1049/cps2.70057","DOIUrl":"https://doi.org/10.1049/cps2.70057","url":null,"abstract":"Time-Sensitive Networking (TSN) is a foundational technology for deterministic communication in industrial automation, automotive and aerospace applications. However, its complexity increases significantly when handling heterogeneous traffic with multiple shaping mechanisms and when networks span multiple administrative domains. This paper presents a targeted state-of-the-art review of analysis and optimisation methods for such multishaper and multidomain TSN systems. We selected representative works through keyword searches on scholarly databases and expert-driven citation analysis, focusing on papers that directly contribute analysis or optimisation methods for these configurations. This review examines four multishaper combinations, that is, time-aware shaper (TAS) with credit-based shaper (CBS), asynchronous traffic shaper (ATS), cyclic queueing and forwarding (CQF) and frame pre-emption (FP), and four multidomain scenarios, that is, TSN-to-TSN federation, TSN-to-Deterministic Networking (DetNet) integration, TSN-to-5G and TSN-to-Wi-Fi convergence. We synthesise 44 representative works in two comparative tables that categorise each study by analysis method, optimisation approach, configuration scope and validation scale. Based on the review and experimental evidence, we summarise 14 open challenge categories across multishaper and multidomain configurations, including the scalability of formal analysis methods, the impact of hardware constraints and the need for unified runtime configuration. Case studies further show that formal analyses become impractical inside large optimisation loops, that joint multishaper optimisation is necessary to avoid infeasible configurations and that large-scale multidomain orchestration remains computationally expensive. We outline future research directions to address these gaps for next-generation industrial systems.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"11 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial Potential Field Based Reward Shaping for Efficient UAV Path Planning via Reinforcement Learning 基于强化学习的人工势场奖励塑造无人机路径规划

IF 1.9

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-06-18 DOI: 10.1049/cps2.70056

Zekai Liu, Tao Zhang, Jingjun Zhou, Tao Xu, Cheng Ben, Keren Zhu

{"title":"Artificial Potential Field Based Reward Shaping for Efficient UAV Path Planning via Reinforcement Learning","authors":"Zekai Liu, Tao Zhang, Jingjun Zhou, Tao Xu, Cheng Ben, Keren Zhu","doi":"10.1049/cps2.70056","DOIUrl":"https://doi.org/10.1049/cps2.70056","url":null,"abstract":"Unmanned aerial vehicles have been widely adopted owing to their compact size, high maneuverability and low cost. Consequently, research on unmanned aerial vehicles guidance, navigation and control systems has emerged as a prominent topic. Path planning constitutes a critical component of guidance, navigation and control systems. To overcome the limitations of existing unmanned aerial vehicles path planning algorithms, including restricted environmental perception, low planning efficiency and diminished learning efficacy—we propose a novel method that integrates the artificial potential field with deep reinforcement learning. We devise a reward strategy that incorporates artificial potential field and path fitting, and construct a hierarchical mechanism that fuses reinforcement learning with classical algorithms. This mechanism effectively optimises the search path, mitigates inefficient exploration and substantially accelerates the path planning process. Finally, experiments conducted in a simulation environment, along with comparative analyses against state-of-the-art techniques, demonstrate the superiority of the proposed method in terms of path planning efficiency.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"11 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ensemble Federated Machine Learning-Based Cybersecurity Situational Awareness in Microgrid Network 基于集成联邦机器学习的微电网网络态势感知

IF 0.8

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-06-12 DOI: 10.1049/cps2.70053

Vivek Kumar Singh, Ethan Tucker, Suman Rath

{"title":"Ensemble Federated Machine Learning-Based Cybersecurity Situational Awareness in Microgrid Network","authors":"Vivek Kumar Singh, Ethan Tucker, Suman Rath","doi":"10.1049/cps2.70053","DOIUrl":"https://doi.org/10.1049/cps2.70053","url":null,"abstract":"Cyber-physical microgrids are vulnerable to stealthy cybersecurity threats that disguise their actions through the exploitation of system knowledge. Such actions can severely impacts microgrids deployed in defense bases, slowing the response time of military forces during national emergencies. Several machine-learning algorithms have been proposed to detect intrusions in the grid networks; however, these traditional machine-learning algorithms lack data privacy and are subject to several adversarial machine-learning threats. This paper proposes a novel federated machine learning (FML)-based three-model framework to detect and identify stealthy data-integrity attacks while ensuring data privacy in microgrid networks. The proposed architecture uses a variational mode decomposition technique to extract derived features from incoming measurement and control datasets. The extraction of these derived features allows FML models to learn minute variations in data patterns that allow them to perform significantly better than the models trained with generic datasets consisting of raw features. Our experimental results show the efficient performance of the proposed methodology against different types of data integrity attacks while considering primary and secondary controllers in microgrids. Further, the applied FML-integrated random forest ensemble algorithm outperforms the existing generic FML algorithms during noisy and noise-free datasets with prediction latencies of only 91–134 µs per sample within the 0.1 s sampling interval and requires communication bandwidth of around ∼8.25 KB/s at the control center and ∼2.7 KB/s per edge client for communication.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"11 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multistage Rotor Imbalance Diagnosis Method for Horizontal-Axis Wind Turbines Based on an Expert System and Improved Convolutional Neural Networks 基于专家系统和改进卷积神经网络的水平轴风力机多级转子不平衡诊断方法

IF 0.8

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-06-12 DOI: 10.1049/cps2.70059

Chang Liu, Kunzhong Lv, Yanjun Sun

{"title":"A Multistage Rotor Imbalance Diagnosis Method for Horizontal-Axis Wind Turbines Based on an Expert System and Improved Convolutional Neural Networks","authors":"Chang Liu, Kunzhong Lv, Yanjun Sun","doi":"10.1049/cps2.70059","DOIUrl":"https://doi.org/10.1049/cps2.70059","url":null,"abstract":"Rotor imbalance in wind turbines can lead to significant safety risks and economic losses, which makes accurate fault analysis essential for reliable operation. However, accurate and robust analysis under varying wind conditions remains challenging. To address these issues, a multistage rotor imbalance analysis framework is proposed, which includes fault detection, fault type classification, fault severity classification and blade localisation. First, a knowledge-based expert system with a multinode decision structure is developed, where nacelle vibration is used for rotor status detection and output power is used for imbalance type classification. Second, a convolutional neural network with training interference that incorporates variable dropout and microbatch training strategies is introduced to improve noise tolerance. Moreover, an adaptive deep interpolation convolutional neural network (DI-DCNN) is proposed for handling wind-induced variations through a multisubnetwork weighting and fusion mechanism. Third, the harmonic components of the nacelle vibration are compensated for using information on the rotor azimuth to achieve blade localisation. Experimental results under various wind conditions demonstrate that the proposed method achieves high accuracy across all the tasks and maintains stable performance under varying turbulence intensities. Moreover, the framework does not require additional sensors and has good generalisation ability. The proposed method provides a unified and robust solution for rotor imbalance detection, classification, and localisation in wind turbines.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"11 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0