IET Collaborative Intelligent Manufacturing最新文献_第2页

Intelligent method framework for 3D surface manufacturing in cloud-edge collaboration architecture 云边协作架构中的三维表面制造智能方法框架

IF 2.5

IET Collaborative Intelligent Manufacturing Pub Date : 2024-07-26 DOI: 10.1049/cim2.12115

Hongming Cai, Yanjun Dong, Min Zhu, Pan Hu, Haoyuan Hu, Lihong Jiang

引用次数: 0

Digital twin-based virtual commissioning for evaluation and validation of a reconfigurable process line 基于数字孪生的虚拟调试，用于评估和验证可重构工艺生产线

IF 2.5

IET Collaborative Intelligent Manufacturing Pub Date : 2024-07-23 DOI: 10.1049/cim2.12111

Suveg V. Iyer, Kuldip Singh Sangwan, Dhiraj

{"title":"Digital twin-based virtual commissioning for evaluation and validation of a reconfigurable process line","authors":"Suveg V. Iyer, Kuldip Singh Sangwan,  Dhiraj","doi":"10.1049/cim2.12111","DOIUrl":"10.1049/cim2.12111","url":null,"abstract":"The benefits of advancements in information and communication technologies have proliferated in manufacturing applications as more industries are migrating towards industry 4.0 compliance. The industry 4.0 process lines should be dynamic and reconfigurable. Digital twin (DT), supported by real-time data, is getting wide acceptance as a tool for monitoring and control of complex processes. Virtual commissioning (VC) has played a vital role in the software-based validation of the control systems. A DT-based VC methodology is proposed to evaluate and validate a reconfigured process line. The proposed new asset is commissioned virtually in the DT environment maintaining other stations and parameters synchronised. The proposed methodology is validated in a modular production system assembly line. A storage and retrieval station is virtually commissioned by the hardware in loop technique in the assembly line DT with a station time error of 1.3% between the VC model and the actual assembly line data. The case study demonstrates the feasibility of the proposed methodology in assessing the impacts due to reconfiguration of a process line. The findings offer significant support to decision makers in taking informed decisions and to reduce unforeseen interruptions resulting from the integration of a new asset with the existing process line.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"6 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141813689","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

RETRACTION: Progress of zinc oxide-based nanocomposites in the textile industry 回顾：氧化锌基纳米复合材料在纺织业中的应用进展

IF 2.5

IET Collaborative Intelligent Manufacturing Pub Date : 2024-07-15 DOI: 10.1049/cim2.12113

{"title":"RETRACTION: Progress of zinc oxide-based nanocomposites in the textile industry","authors":"","doi":"10.1049/cim2.12113","DOIUrl":"https://doi.org/10.1049/cim2.12113","url":null,"abstract":"RETRACTION: R. Huang, S. Zhang, W. Zhang, X. Yang, “Progress of Zinc Oxide-Based Nanocomposites in the Textile Industry,” IET Collaborative Intelligent Manufacturing 3, no. 3 (2021): 281–289. https://doi.org/10.1049/cim2.12029.The above article, published online on 24 May 2021 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editors-in-Chief; Liang Gao and Weiming Shen; the Institution of Engineering and Technology; and John Wiley & Sons Ltd.The retraction has been agreed on after concerns about this manuscript were raised by a third party. An investigation revealed several inconsistencies regarding the experiments described and the results presented. Furthermore, multiple references are unrelated to this manuscript and are considered insufficient to support the corresponding statements in the text. The experimental methods are not described in detail, and so the research is not comprehensible for the readers, the experiments are not reproducible, and the conclusions are considered invalid. The authors have been informed of the decision to retract.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"6 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141624548","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

RETRACTION: Knowledge map visualization of technology hotspots and development trends in China's textile manufacturing industry 返回：中国纺织制造业技术热点与发展趋势知识图谱可视化

IF 2.5

IET Collaborative Intelligent Manufacturing Pub Date : 2024-07-15 DOI: 10.1049/cim2.12112

引用次数: 0

Overcoming the knowledge gaps in early-stage servitization journey: A guide for small and medium enterprises 克服早期服务化历程中的知识差距：中小企业指南

IF 2.5

IET Collaborative Intelligent Manufacturing Pub Date : 2024-07-12 DOI: 10.1049/cim2.12106

Mario Rapaccini, Federico Adrodegari, Giuditta Pezzotta, Nicola Saccani

引用次数: 0

An orchestrated IoT-based blockchain system to foster innovation in agritech 基于物联网的协调区块链系统促进农业技术创新

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2024-06-08 DOI: 10.1049/cim2.12109

Igor Tasic, Maria-Dolores Cano

{"title":"An orchestrated IoT-based blockchain system to foster innovation in agritech","authors":"Igor Tasic, Maria-Dolores Cano","doi":"10.1049/cim2.12109","DOIUrl":"https://doi.org/10.1049/cim2.12109","url":null,"abstract":"Agritech uses advanced technologies to boost the efficiency, sustainability, and productivity of farming. The Internet of Things (IoT) in agriculture has brought sensors and networked technology to gather and analyse environmental and crop data, enabling precision farming that optimises resource usage and increases yields. Yet, current agricultural methods suffer from unsecured and decentralised data management, causing inefficiencies and complicating traceability across the supply chain. The integration of IoT with blockchain technology is seen as a promising solution to enhance data-driven agriculture. Blockchain provides a secure, decentralised, and transparent ledger that enhances data integrity, reduces fraud, and improves traceability, which complements IoT applications. The authors detail the development of an innovative system that orchestrates IoT and blockchain technologies to facilitate the adoption of new technologies in agriculture and overcomes the lacked of comprehensive data connectivity. It outlines a conceptual framework and its preliminary empirical implementation. The system consists of three integrated layers: the IoT layer, which creates digital twins of field crops; the blockchain layer, which secures and manages data from the field and external stakeholders for dynamic applications such as track and tracing; and the orchestration layer, which fuses physical and digital data to optimise business models, enhance supply chain productivity, and support governmental policy-making, thereby improving field productivity and food sector innovation.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"6 2","pages":""},"PeriodicalIF":8.2,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141292638","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

Uncertainty-aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm 机器学习与启发式算法相结合的不确定性感知核电涡轮机振动故障诊断方法

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2024-06-07 DOI: 10.1049/cim2.12108

Ruirui Zhong, Yixiong Feng, Puyan Li, Xuanyu Wu, Ao Guo, Ansi Zhang, Chuanjiang Li

{"title":"Uncertainty-aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm","authors":"Ruirui Zhong, Yixiong Feng, Puyan Li, Xuanyu Wu, Ao Guo, Ansi Zhang, Chuanjiang Li","doi":"10.1049/cim2.12108","DOIUrl":"https://doi.org/10.1049/cim2.12108","url":null,"abstract":"Nuclear power turbine fault diagnosis is an important issue in the field of nuclear power safety. The numerous state parameters in the operation and maintenance of nuclear power turbines are collected, forming a complex high-dimensional feature space. These high-dimensional feature spaces contain redundant information, which increases the training cost and reduces the recognition accuracy and efficiency of the fault diagnosis model. To address the aforementioned challenges, a vibration fault diagnosis algorithm in nuclear power turbines is proposed. First, a long short-term memory-based denoising autoencoder (LDAE) is designed to enhance the capability of uncertainty awareness. Then, a feature extraction method integrating variational mode decomposition (VMD), L-cliffs-based effective mode selection, and sample entropy is devised to extract the latent features from the complex high-dimensional feature space. Furthermore, using extreme gradient boosting (XGBoost) as the classifier, LDAE-VMD-XGBoost model is constructed for fault diagnosis of nuclear power turbines. Considering the impact of multiple hyperparameters of LDAE-VMD-XGBoost model on the performance, the pathfinder algorithm is used to optimise the model hyperparameter settings and improve the fault diagnosis accuracy. Experimental results demonstrate the performance of the proposed improved LDAE-VMD-XGBoost in accurate nuclear power turbine vibration fault diagnosis.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"6 3","pages":""},"PeriodicalIF":8.2,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286942","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

Adaptive DFL-based straggler mitigation mechanism for synchronous ring topology in digital twin networks 数字孪生网络中基于同步环拓扑的自适应 DFL 流浪者缓解机制

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2024-06-07 DOI: 10.1049/cim2.12107

Qazi Waqas Khan, Chan-Won Park, Rashid Ahmad, Atif Rizwan, Anam Nawaz Khan, Sunhwan Lim, Do Hyeun Kim

{"title":"Adaptive DFL-based straggler mitigation mechanism for synchronous ring topology in digital twin networks","authors":"Qazi Waqas Khan, Chan-Won Park, Rashid Ahmad, Atif Rizwan, Anam Nawaz Khan, Sunhwan Lim, Do Hyeun Kim","doi":"10.1049/cim2.12107","DOIUrl":"https://doi.org/10.1049/cim2.12107","url":null,"abstract":"Decentralised federated learning (DFL) transforms collaborative energy consumption prediction using distributed computation across a large network of edge nodes, ensuring data confidentiality by eliminating central data aggregation. Preserving individual privacy in energy forecasting is paramount, as it safeguards personal data from unauthorised examination. This highlights the importance of effectively handling local data to provide privacy protection. The authors proposed a DFL framework for residential energy forecasting, focusing on improving the performance and convergence of the collaborative model. The proposed framework enables local training of the long short-term memory model with real-time household energy data in a ring topology. Importantly, the framework addresses the issue of straggler nodes, nodes that lag in computation or communication, by proposing a heuristic straggler identification and mitigation mechanism to reduce their negative impact on overall system performance and communication efficiency. This approach improves collaborative energy prediction performance and ensures an overall reduction in waiting time, thus improving the convergence performance. Experimental results consistently demonstrate a low mean absolute error ranging from 3 to 3.2 across all edge nodes. The empirical findings unequivocally illustrate the efficiency of the proposed DFL architecture, highlighting its ability to improve communication efficiency and concurrently enhance performance.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"6 3","pages":""},"PeriodicalIF":8.2,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286954","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

Reinforcement learning driven moth-flame optimisation algorithm for solving numerical optimisation problems 用于解决数值优化问题的强化学习驱动蛾焰优化算法

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2024-05-28 DOI: 10.1049/cim2.12101

Fuqing Zhao, Yuqing Du, Qiaoyun Wang

引用次数: 0

YOLO-DFT: An object detection method based on cloud data fusion and transfer learning for power system equipment maintenance YOLO-DFT：基于云数据融合和迁移学习的电力系统设备维护对象检测方法

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2024-05-13 DOI: 10.1049/cim2.12104

Kai Wang, Xu Zhang, Yifan Sun, Tianyi Xu, Jiqiao Li, Song Cao

{"title":"YOLO-DFT: An object detection method based on cloud data fusion and transfer learning for power system equipment maintenance","authors":"Kai Wang, Xu Zhang, Yifan Sun, Tianyi Xu, Jiqiao Li, Song Cao","doi":"10.1049/cim2.12104","DOIUrl":"https://doi.org/10.1049/cim2.12104","url":null,"abstract":"Object detection techniques have been widely used in power system equipment maintenance. However, in power systems, the accuracy of object detection is limited by the scarcity of publicly available datasets and the lack of scene pertinence. In order to solve these problems, an object detection method based on cloud data fusion and transfer learning (YOLO-DFT) for power system equipment maintenance is proposed. Illustratively, YOLO-DFT focuses on the object detection task involving birds and humans, generating a substantial and resilient human-bird dataset through cloud-based data fusion to compensate for the dearth of public datasets in the power system domain. By seamlessly integrating the YOLOv5 algorithm with a transfer learning strategy, a targeted detection mechanism for specific locations is meticulously formulated. The experimental results demonstrate that YOLO-DFT effectively addresses object detection challenges in power systems, achieving a Mean Average Precision (MAP) measure of 0.925 across all classes, thereby providing a valuable reference for the maintenance of power system equipment.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"6 2","pages":""},"PeriodicalIF":8.2,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140919244","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