IET Collaborative Intelligent Manufacturing最新文献_第8页

Resource-Efficient Anomaly Detection in Industrial Control Systems With Quantized Recurrent Variational Autoencoder 基于量化循环变分自编码器的工业控制系统资源高效异常检测

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-04-25 DOI: 10.1049/cim2.70032

Daniel Fährmann, Malte Ihlefeld, Arjan Kuijper, Naser Damer

{"title":"Resource-Efficient Anomaly Detection in Industrial Control Systems With Quantized Recurrent Variational Autoencoder","authors":"Daniel Fährmann, Malte Ihlefeld, Arjan Kuijper, Naser Damer","doi":"10.1049/cim2.70032","DOIUrl":"10.1049/cim2.70032","url":null,"abstract":"This work presents a novel solution for multivariate time series anomaly detection in industrial control systems (ICSs), specifically tailored for resource-constrained environments. At its core, the quantized gated recurrent unit variational autoencoder (Q-GRU-VAE) architecture, a significant evolution from conventional methods, offers an extremely lightweight yet highly effective solution. By integrating gated recurrent units (GRUs) in place of long short-term memory (LSTM) cells within a variational autoencoder (VAE) framework, and employing channel-wise dynamic post-training quantization (DPTQ), this model dramatically reduces hardware resource demands. The proposed solution exhibits performance on par with existing methods on the widely used secure water treatment (SWaT) and water distribution (WADI) benchmarks, while being tailored towards applications where computational resources are limited. This dual achievement of minimal resource consumption and preserved model efficacy paves the way for deploying advanced anomaly detection in resource-constrained environments, marking a significant leap forward in enhancing the resilience and efficiency of ICSs.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875614","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

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-04-25

引用次数: 0

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-04-24

引用次数: 0

Integrated Optimisation of Shop Scheduling and Machine Layout for Discrete Manufacturing Considering Uncertain Events Based on an Improved Immune Genetic Algorithm 考虑不确定事件的离散制造车间调度与机器布局的改进免疫遗传算法集成优化

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-04-24 DOI: 10.1049/cim2.70022

Zhaoxi Hong, Yixiong Feng, Amir M. Fathollahi-Fard, Zhiwu Li, Bingtao Hu, Jianrong Tan

{"title":"Integrated Optimisation of Shop Scheduling and Machine Layout for Discrete Manufacturing Considering Uncertain Events Based on an Improved Immune Genetic Algorithm","authors":"Zhaoxi Hong, Yixiong Feng, Amir M. Fathollahi-Fard, Zhiwu Li, Bingtao Hu, Jianrong Tan","doi":"10.1049/cim2.70022","DOIUrl":"10.1049/cim2.70022","url":null,"abstract":"Shop scheduling and machine layout are two important aspects of discrete manufacturing. There are strong coupling relationships between them, but they were conducted separately in the past, which significantly limits the production performance improvement of discrete manufacturing. At the same time, in the actual process of workshop production, uncertain events not only often occur but also may make the existing scheduling schemes no longer suitable. To address such issues, the integrated optimisation of shop scheduling and machine layout for discrete manufacturing considering uncertain events is proposed in this paper, where the minimum material handling cost, the maximum space utilisation rate and the minimum production completion time are selected as the optimisation objectives. An improved immune genetic algorithm is designed to solve the corresponding mathematical model efficiently by dual-layer encoding, which is good at global optimisation. Moreover, multistrategy redundancy-aware workshop rescheduling is performed to respond to uncertain events that are regarded as production disturbances. The rationality and superiority of the proposed method are verified by a numerical case study of a discrete manufacturing workshop for wood–plastic composite materials with its integrated optimisation of shop scheduling and machine layout, as well as its rescheduling schemes under machine failures.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865896","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 Novel DQN-Based Hybrid Algorithm for Integrated Scheduling and Machine Maintenance in Dynamic Flexible Job Shops 一种基于dqn的动态柔性作业车间调度与机器维护集成混合算法

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-04-21 DOI: 10.1049/cim2.70028

Nanxing Chen, Yong Chen, Wenchao Yi, Zhi Pei

{"title":"A Novel DQN-Based Hybrid Algorithm for Integrated Scheduling and Machine Maintenance in Dynamic Flexible Job Shops","authors":"Nanxing Chen, Yong Chen, Wenchao Yi, Zhi Pei","doi":"10.1049/cim2.70028","DOIUrl":"10.1049/cim2.70028","url":null,"abstract":"This paper focuses on the dynamic flexible job shop scheduling problem with constrained maintenance resources (DFJSP-CMR), a pressing challenge in modern manufacturing systems. As traditional rigid scheduling models fall short in meeting the demands of today's dynamic production environments, there is a growing need for intelligent approaches that can seamlessly integrate production scheduling and maintenance planning under resource limitations. To tackle this challenge, we propose a novel hybrid algorithm aimed at minimising makespan while addressing machine deterioration, unexpected failures and constrained maintenance resources. The core of our approach is a deep Q-network with maintenance insertion algorithm (DQN-MI) specifically designed for efficient maintenance scheduling. The algorithm features a 5×3 action space, constructed as compound rules, along with a reward structure that balances machine utilisation efficiency with effective maintenance operations. Extensive computational experiments conducted on diverse problem instances demonstrate that DQN-MI delivers superior performance, further validating the effectiveness and versatility of the proposed method in addressing complex scheduling challenges while maintaining the stability and reliability of manufacturing systems. This research contributes to the advancement of intelligent manufacturing by presenting a robust and practical solution for the integrated management of production scheduling and maintenance planning.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857028","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

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-04-21

引用次数: 0

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-04-05

引用次数: 0

Scheduling Reentrant FlowShops: Reinforcement Learning-guided Meta-Heuristics 调度可重入流商店：强化学习引导的元启发式

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-04-05 DOI: 10.1049/cim2.70029

Jingwen Yuan, Kaizhou Gao, Adam Slowik, Benxue Lu, Yanan Jia

{"title":"Scheduling Reentrant FlowShops: Reinforcement Learning-guided Meta-Heuristics","authors":"Jingwen Yuan, Kaizhou Gao, Adam Slowik, Benxue Lu, Yanan Jia","doi":"10.1049/cim2.70029","DOIUrl":"10.1049/cim2.70029","url":null,"abstract":"The reentrant flowshop scheduling problems (RFSP) are ubiquitous in high-tech industries such as semiconductor manufacturing and liquid crystal display (LCD) production. Given the complexity of RFSP, it is significant to improve the production efficiency using effective intelligent optimisation techniques. In this study, four meta-heuristics assisted by two reinforcement learning (RL) algorithms are proposed to minimise the maximum completion time (makespan) for RFSP. First, a mathematical model for RFSP is established. Second, four meta-heuristics are improved. The Nawaz–Enscore–Ham (NEH) heuristic is utilised for population initialisation. Based on the problem characteristics, we design six local search operators, which are integrated into the four meta-heuristics. Third, two RL algorithms, Q-learning and state–action-reward–state–action (SARSA), are employed to select the appropriate local search operator during iterations to enhance the convergence in a local space. Finally, the results of solving 72 instances indicate that the proposed algorithms perform effectively. The RL-guided local search can significantly enhance the overall performance of the four meta-heuristics. In particular, the artificial bee colony algorithm (ABC) combined with SARSA-guided local search yields the highest performance.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784303","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

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-03-14

引用次数: 0

Comprehensive Systematic Literature Review on Cognitive Workload: Trends on Methods, Technologies and Case Studies 认知负荷的综合系统文献综述：方法、技术和案例研究的趋势

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-03-14 DOI: 10.1049/cim2.70025

Andrea Lucchese, Antonio Padovano, Francesco Facchini

{"title":"Comprehensive Systematic Literature Review on Cognitive Workload: Trends on Methods, Technologies and Case Studies","authors":"Andrea Lucchese, Antonio Padovano, Francesco Facchini","doi":"10.1049/cim2.70025","DOIUrl":"10.1049/cim2.70025","url":null,"abstract":"Cognitive workload (CWL) assessment has gained increasing importance in Industry 4.0 and 5.0 settings where human–machine interactions are becoming more complex. Despite growing attention, a comprehensive CWL assessment that integrates methodologies, technologies and case studies is still lacking. This study reviews 69 articles related to the CWL assessment, selected from the Scopus database. The review identifies five primary methodologies for the CWL assessment: physiological measures, subjective evaluation (e.g., NASA-TLX), performance evaluation, cognitive load models and multimodal approaches. The analysis shows an increasing trend towards multimodal approaches that combine subjective assessment methods with physiological measures obtained from electroencephalography, eye tracking and heart rate monitoring devices. Additionally, emerging technologies such as advanced sensors and specialised equipment are increasingly considered in case studies that address the CWL assessment in current work environments. Results reveal significant advancements in physiological and multimodal assessment methods, particularly emphasising real-time monitoring capabilities and context-specific applications. Case studies underscore the key role of CWL management in assembly, maintenance and construction tasks, demonstrating its impact on performance, safety and adaptability in dynamic environments. This review establishes a framework for advancing CWL research by addressing methodological limitations and proposing future research directions, including the development of personalised, adaptive systems for real-time workload management.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622417","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