IET Collaborative Intelligent Manufacturing最新文献_第4页

A conceptual framework proposal for the implementation of Prognostic and Health Management in production systems 关于在生产系统中实施预测和健康管理的概念框架建议

IF 2.5

IET Collaborative Intelligent Manufacturing Pub Date : 2024-10-03 DOI: 10.1049/cim2.12122

Raffaele Abbate, Chiara Franciosi, Alexandre Voisin, Marcello Fera

{"title":"A conceptual framework proposal for the implementation of Prognostic and Health Management in production systems","authors":"Raffaele Abbate, Chiara Franciosi, Alexandre Voisin, Marcello Fera","doi":"10.1049/cim2.12122","DOIUrl":"https://doi.org/10.1049/cim2.12122","url":null,"abstract":"Prognostic and Health Management (PHM) is an emerging maintenance concept that is highly regarded by the scientific community and practitioners, as its adoption can bring economic, technical and environmental benefits to a company. PHM fully reflects the smart maintenance paradigm encompassing data collection, data manipulation, state detection, health assessment, prognostic assessment and advisory generation. Despite the undeniable benefits, there is still a large gap between the scientific and the real world. Several authors have investigated on the barriers to PHM implementation for companies, highlighting among them the lack of systematic approaches to its design and implementation. As a first contribution to this topic, the authors conducted a systematic literature review (SLR) to investigate the use of Decision Support Systems (DSSs) to support the PHM implementation. The SLR highlighted that few DSS had been developed and were limited to critical unit identification, maintenance strategy selection and data acquisition phase of PHM. Therefore, a conceptual framework for PHM implementation was provided as a second contribution. This framework summarises the decisions that should be addressed by a practitioner wishing to implement PHM services; moreover, it could lay the foundations for the development/improvement of the missing/existing DSSs for PHM implementation.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"6 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12122","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428882","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

Improved hybrid estimation of distribution algorithm for distributed parallel assembly permutation flow shop scheduling problem 分布式并行装配排列流水车间调度问题的改进型混合估计分配算法

IF 2.5

IET Collaborative Intelligent Manufacturing Pub Date : 2024-07-31 DOI: 10.1049/cim2.12116

Lizhen Du, Xintao Wang, Jiaqi Tang, Chuqiao Xu, Guanxing Qin

{"title":"Improved hybrid estimation of distribution algorithm for distributed parallel assembly permutation flow shop scheduling problem","authors":"Lizhen Du, Xintao Wang, Jiaqi Tang, Chuqiao Xu, Guanxing Qin","doi":"10.1049/cim2.12116","DOIUrl":"https://doi.org/10.1049/cim2.12116","url":null,"abstract":"Distributed assembly permutation flow shop scheduling problem is the hot spot of distributed pipeline scheduling research; however, parallel assembly machines are often in the assembly stage. Therefore, we propose and study distributed parallel assembly permutation flow shop scheduling problem (DPAPFSP). This aims to enhance the efficiency of multi-factory collaborative production in a networked environment. Initially, a corresponding mathematical model was established. Then, an improved hybrid distribution estimation algorithm was proposed to minimize the makespan. The algorithm adopts a single-layer permutation encoding and decoding strategy based on the rule of the Earliest Finished Time. A local neighbourhood search based on critical paths is performed for the optimal solution using five types of neighborhood design. A dual sampling strategy based on repetition rates was introduced to ensure the diversity of the population in the later periods of iteration. Simulated annealing searching was applied to accelerate the decline of optimal value. Finally, we conduct simulation experiments using 900 arithmetic cases and compare the simulation experimental data of this algorithm with the other four existing algorithms. The analysis results demonstrate this improved algorithm is very effective and competitive in solving the considered DPAPFSP.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"6 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12116","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968445","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

Imbalanced classification in faulty turbine data: New proximal policy optimisation 故障涡轮机数据中的不平衡分类：新的近端策略优化

IF 2.5

IET Collaborative Intelligent Manufacturing Pub Date : 2024-07-31 DOI: 10.1049/cim2.12114

Mohammad Hossein Modirrousta, Mahdi Aliyari Shoorehdeli, Mostafa Yari

{"title":"Imbalanced classification in faulty turbine data: New proximal policy optimisation","authors":"Mohammad Hossein Modirrousta, Mahdi Aliyari Shoorehdeli, Mostafa Yari","doi":"10.1049/cim2.12114","DOIUrl":"https://doi.org/10.1049/cim2.12114","url":null,"abstract":"In industrial and real-world systems, recognising errors and adopting the best approaches are gaining relevance. The authors’ goal is to identify artificial intelligence apps that provide the most reliable and valuable data-based fault detection techniques. A system for fault identification is presented based on reinforcement learning and proximal policy optimisation (PPO). Due to the lack of fault data, one of the key issues with the standard policy is its inability to recognise fault classes; this issue was resolved by modifying the cost equation. Using improved PPO, the authors can improve performance, address data imbalances, and forecast possible failures more accurately. The approach utilises policy-based optimisation, which offers several advantages. Firstly, it directly optimises the advantage quantity, and secondly, it ensures the stability of function approximation. The authors have studied two different turbines in Iran and collected data from them separately when a fault occurred. To demonstrate the efficiency of our algorithm, the authors have included the third and fourth datasets as cyber attack benchmarks. When the authors’ proposed policy is adopted, all evaluation metrics will improve by 3%–4% as compared to the previous policy in the first benchmark, between 20% and 55% in the second benchmark, between 6% and 14% in the third benchmark, and between 4% and 5% in the fourth benchmark, with improved results and prediction times compared to existing studies.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"6 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12114","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968430","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

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