Journal of Industrial Information Integration最新文献

{"title":"A sensorless admittance control system for physical human-robot interaction on a two-wheeled differential drive mobile platform","authors":"Jinxiang Deng ,&nbsp;Li An ,&nbsp;Lingyun Luo ,&nbsp;Zhuo Zou ,&nbsp;Jie Lu ,&nbsp;Li Gong ,&nbsp;Li Da Xu ,&nbsp;Zhongxue Gan ,&nbsp;Yuxiang Guan","doi":"10.1016/j.jii.2025.101046","DOIUrl":"10.1016/j.jii.2025.101046","url":null,"abstract":"<div><div>Physical human-robot interaction (pHRI) increasingly demands robots that are both compliant and capable of handling substantial payloads. While admittance control has been successfully applied to mobile manipulators, compliant control for the mobile platforms themselves—particularly for widely deployed, non-holonomic differential-drive types—remains an underexplored challenge. This paper addresses this gap by proposing a novel, sensorless admittance control system specifically designed for two-wheeled differential-drive mobile platforms. The core contributions are threefold. First, a detailed kinematic and dynamic analysis is conducted to establish the system's theoretical foundation. Second, we develop a resultant external force/torque estimator that requires no additional sensors, utilizing only motor currents and wheel encoder data, thereby achieving zero hardware cost. Third, we introduce an autonomous payload parameter identification method with k-means for data selection, enabling the system to adapt to unknown and variably positioned loads. Real-world experiments demonstrate that the proposed controller reduces the required human guiding force by approximately 50% compared to the original system. The proposed controller successfully reconciles high compliance with high load capacity, handling payloads ranging from 73 kg to 173 kg. This work provides a systematic, cost-effective solution for deploying compliant, high-payload mobile platforms in future industrial and domestic pHRI applications.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101046"},"PeriodicalIF":10.4,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-layer knowledge and data-driven integrated framework for smart manufacturing process: An experimental application for aerospace sheet metal process planning","authors":"Murillo Skrzek ,&nbsp;Anderson Luis Szejka ,&nbsp;Fernando Mas","doi":"10.1016/j.jii.2025.101045","DOIUrl":"10.1016/j.jii.2025.101045","url":null,"abstract":"<div><div>The aerospace manufacturing industry faces substantial complexity, particularly in the aircraft manufacturing process, which requires integrating advanced components and systems with diverse geometries and materials. This environment necessitates robust information systems to manage information exchange across the product life cycle and reduce disruptions during project development. Traditional manufacturing systems struggle to integrate diverse automation technologies and maintain efficiency in highly customised and technologically complex aerospace production. Interferences caused by project changes can lead to increased costs, longer time commitments, and greater environmental impacts. Based on this context, this research proposes a multi-layer knowledge and data-driven integrated framework to seamlessly integrate digital and physical technologies, facilitating communication and transparency across the complex manufacturing process. It supports manufacturing tasks such as process planning, cost estimation, and quality assurance, ensuring the capture and utilisation of explicit and implicit knowledge. Implementing the multi-layer knowledge and data-driven integrated framework enhances manufacturing efficiency, reduces costs, and improves product quality in the aerospace industry. An experimental case demonstrated the ability to store data and knowledge in a structured way, thereby generating different manufacturing plans, supporting process decision-making, and improving the 72.1% efficiency of plan generation with human validation. Future research will focus on validating the manufacturing plan generated from existing manual process plans, enabling optimisation of manufacturing according to the most suitable plan presented, aiming to refine it further and expand its applicability in the aerospace sector.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101045"},"PeriodicalIF":10.4,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DPDC-ILKM: A multi-agent integrated large knowledge model for intelligent maintenance of industrial swarm robotics","authors":"Jiaxian Chen ,&nbsp;Yujie Xu ,&nbsp;Jie Tang ,&nbsp;Xuemiao Xu ,&nbsp;Ruqiang Yan ,&nbsp;Zhixin Yang ,&nbsp;Weihua Li","doi":"10.1016/j.jii.2025.101044","DOIUrl":"10.1016/j.jii.2025.101044","url":null,"abstract":"<div><div>The rapid advancement of large language models (LLMs) has introduced transformative capabilities into industrial intelligence. However, their direct application to the prognostics and health management (PHM) of industrial swarm robotics remains limited due to the lack of specialized maintenance knowledge and insufficient functional integration. Embodied Intelligence (EI), with its capacities for perception, cognition, reasoning, decision-making, and iterative evolution, offers a promising solution to these challenges. Therefore, a multi-agent integrated large knowledge model framework, termed Diagnosis-Prediction-Decision-Control-ILKM (DPDC-ILKM), is proposed to empower intelligent maintenance in industrial swarm robotics. In the DPDC-ILKM framework, a high-reliability industrial large knowledge model is first constructed by integrating operational maintenance records and corpus knowledge from different industrial robotics to adapt to the PHM tasks of diverse individual robots. Second, a multi-agent EI maintenance system is designed to provide operation and maintenance services, including diagnostic, prognostic, decision-making, and control functions. To support the continual improvement of DPDC-ILKM, a self-evolution mechanism is introduced, enabling adaptive learning and continuous optimization in dynamic industrial environments. Finally, the key challenges and future directions are discussed to support the advancement of EI-enabled industrial artificial intelligence. This work presents a unique framework that combines LLMs with EI for industrial maintenance, offering a novel perspective and technical foundation for intelligent maintenance of industrial swarm robotics.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101044"},"PeriodicalIF":10.4,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical Modeling and Analysis of Power Grid Cyber-Physical Systems: Application and Validation of HM-GCPS","authors":"Yang-Rong Chen ,&nbsp;Jun-E Li ,&nbsp;Jie Zhang ,&nbsp;Yu-Fei Wang ,&nbsp;Ting Zhao","doi":"10.1016/j.jii.2025.101042","DOIUrl":"10.1016/j.jii.2025.101042","url":null,"abstract":"<div><div>This paper is the second part of a two-part series. In Part Ⅰ, the hierarchical model (i.e., HM-GCPS) of power grid cyber-physical system (GCPS) is established. Part II presents the application method of HM-GCPS in Part I used to solve a specific problem, and verifies the effectiveness of HM-GCPS by an example. First, a modeling and analysis architecture, describing the problems that can be analyzed by HM-GCPS, is proposed. To verify the feasibility and effectiveness of HM-GCPS, the risk propagation analysis process of GCPS cyber space is presented based on HM-GCPS. Then, taking a provincial power dispatch data network (PDDN) as an example, the failure event chains under five cyber-attack scenarios are analyzed and deduced. These five scenarios target all layers of the GCPS cyber space. Meanwhile, the impacts of the five cyber-attack scenarios on GCPS communication services are simulated. The results show that HM-GCPS is feasible and effective when it is used for analyzing the risk propagation of GCPS cyber space.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101042"},"PeriodicalIF":10.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automating appliance verification in facilities management using a denoised Voltage-Current feature extraction and classification pipeline","authors":"Socretquuliqaa Lee ,&nbsp;Faiyaz Doctor ,&nbsp;Mohammad Hossein Anisi ,&nbsp;Shashank Goud ,&nbsp;Xiao Wang","doi":"10.1016/j.jii.2025.101040","DOIUrl":"10.1016/j.jii.2025.101040","url":null,"abstract":"<div><div>Facilities Management (FM) companies can use load monitoring of electrical appliances (assets) to track energy consumption and predictive maintenance. Reliable algorithms are needed to automatically identify or verify appliances through their energy signatures to improve efficiencies during installation and inspection tasks. Most approaches rely on Voltage-Current (V-I) trajectory. These features are extracted from steady-state current and voltage signals. However, these methods often assume signals are uniformly sampled. In real-world conditions, this assumption does not always hold, leading to misclassified steady-state events when signals are noisy. This paper introduces a novel feature extraction and classification pipeline to ensure the validity of detected steady-state events. The approach measures the approximate entropy of current signals and their correlation with voltage to extract denoised features for appliance type classification. The proposed pipeline is evaluated on a large-scale real-world operational dataset spanning multiple appliance categories. We demonstrate that the extracted denoised features significantly improve the performance of Machine Learning (ML) models used for appliance type classification. Finally, we present a deployment framework for FM settings, enabling digital cataloguing of appliances informing businesses on sustainable choices for appliance requirements.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101040"},"PeriodicalIF":10.4,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge graph-driven fault diagnosis for aviation equipment: Integrating improved joint extraction with large language model","authors":"Lunyong Li ,&nbsp;Auwal Haruna ,&nbsp;Wanming Ying ,&nbsp;Khandaker Noman ,&nbsp;Yongbo Li","doi":"10.1016/j.jii.2025.101039","DOIUrl":"10.1016/j.jii.2025.101039","url":null,"abstract":"<div><div>Aviation equipment fault diagnosis faces significant challenges due to the complexity of systems, the scarcity of high-quality labeled data, and the critical need for interpretability in maintenance decisions. While Knowledge Graph (KG) offers a promising solution for structured knowledge management, their broader application is impeded by limitations in knowledge extraction methods from unstructured texts and inefficient retrieval mechanisms. To address these gaps, this study proposes an innovative KG method that integrates an enhanced joint extraction model with Large Language Model (LLM) for aviation equipment fault diagnosis. To overcome the bottlenecks of high complexity and low efficiency in constructing KGs for aviation fault diagnosis, this study proposes an Aviation Equipment Maintenance Cascade Binary Tagging (AemCASREL) model optimized with Bidirectional Encoder Representation from Transformers (BERT) fine-tuning and attention enhancement. This model extracts fault entities and relations from diverse unstructured sources, such as aircraft maintenance manuals and equipment logs, to build a KG database in Neo4j. Additionally, a method integrating an LLM with the KG database is introduced, enhancing the model’s generation ability, enabling intelligent question-answering, and offering robust domain knowledge support for fault diagnosis. The experimental evaluation, using both self-built and public datasets, demonstrates the improved model's superiority over the baseline. On the self-built dataset, the F1 score rises from 0.907 to 0.968, and on the public dataset, it increases from 0.907 to 0.980. The integration of LLM and KG enhances the accuracy and intelligence of the question-answering system for aircraft fault diagnosis and maintenance, making it more adaptable to complex faults. This study provides a feasible knowledge-driven paradigm for multi-source information fusion and integration in complex industrial scenarios.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101039"},"PeriodicalIF":10.4,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-scale digital twin model reconstruction method based on compatibility and exclusivity mechanisms","authors":"Xiaojian Wen ,&nbsp;Wenchao Bian ,&nbsp;Shimin Liu ,&nbsp;Jie Wen ,&nbsp;Jinsong Bao ,&nbsp;Dan Zhang","doi":"10.1016/j.jii.2025.101041","DOIUrl":"10.1016/j.jii.2025.101041","url":null,"abstract":"<div><div>With the deepening of digital transformation in the manufacturing industry, digital twin technology has become a key enabler for enhancing the flexibility and intelligent reconfiguration of manufacturing systems. However, the current construction of digital-twin-based production lines still relies heavily on manual expertise and lacks a systematic approach capable of automatically selecting and configuring appropriate components under task constraints. To address this issue, this paper proposes a multi-scale digital twin model reconstruction method based on compatibility and exclusivity mechanisms. The proposed approach establishes a component selection framework that integrates functional, spatial, and associative semantics, and further incorporates the temporal dimension to capture the dynamic evolution of component compatibility. This enables task-driven dynamic reconfiguration and adaptive optimization of spatial layouts. Experimental results demonstrate that the proposed methyod significantly improves component selection accuracy and the level of automation in configuration processes, while ensuring functional compatibility and spatial coordination. The study provides both theoretical support and an engineering-oriented solution for multi-scale intelligent planning and decision optimization in complex manufacturing systems.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101041"},"PeriodicalIF":10.4,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-efficient task offloading in the Industrial Internet of Things: A Lyapunov-guided multi-agent deep reinforcement learning approach","authors":"Zihang Yu ,&nbsp;Zhenjiang Zhang ,&nbsp;Sherali Zeadally","doi":"10.1016/j.jii.2025.101037","DOIUrl":"10.1016/j.jii.2025.101037","url":null,"abstract":"<div><div>Multi-access Edge Computing (MEC) integrated with the Industrial Internet of Things (IIoT) is vital for intelligent manufacturing and industrial automation because it enables low-latency and high-efficiency task offloading from resource-limited devices to an edge server. However, dynamic wireless channels and stochastic task arrivals introduce significant uncertainties, leading to queuing delays, inefficient resource utilization, and high energy consumption. Moreover, the lack of future system information makes real-time offloading decisions particularly challenging. To address these issues, we construct both task queues and delay-aware virtual queues, and we formulate a stochastic optimization problem for joint task offloading and resource allocation. The objective is to minimize long-term energy consumption while ensuring queue stability and satisfying task deadline constraints. To solve this problem, we propose a novel Lyapunov-guided multi-agent deep reinforcement learning framework (LYMADDPG), which integrates Lyapunov optimization with Multi-Agent Deep Deterministic Policy Gradient (MADDPG). Specifically, we use Lyapunov optimization to transform delay constraints into a virtual queue stability control problem, converting the original long-term problem into a series of per-slot optimizations. Next, we use MADDPG to learn optimal offloading and resource allocation policies in a distributed and adaptive manner. Extensive simulation results demonstrate that our method significantly outperforms baseline algorithms in reducing energy consumption, ensuring queue stability, and meeting task deadlines. These results confirm the practical effectiveness of our approach and highlight its strong potential for real-world deployment in MEC-enabled IIoT systems.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101037"},"PeriodicalIF":10.4,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Type-II robotic partial disassembly line balancing problem and MIP-based bi-stage genetic neighborhood search algorithm","authors":"Wei Liang ,&nbsp;Zeqiang Zhang ,&nbsp;Dan Ji ,&nbsp;Haiye Chen ,&nbsp;Yan Li ,&nbsp;Qiyao Duan ,&nbsp;Zongxing He","doi":"10.1016/j.jii.2025.101036","DOIUrl":"10.1016/j.jii.2025.101036","url":null,"abstract":"<div><div>Once a disassembly line is constructed, it typically remains unchanged for an extended period. Consequently, the disassembly of end-of-life (EOL) electronic and electrical appliances for subsequent orders must be planned based on the existing production line configuration after completing the previous order of EOL electronic and electrical appliances. To address this challenge, this study proposed a type-II robotic partial disassembly line balancing (II-RPDLB) problem, leveraging advanced robot techniques. In addition, a mixed integer programming (MIP) model was developed according to the characteristics of the II-RPDLB problem. Furthermore, this study designed an MIP-based bi-stage genetic neighborhood search algorithm (bi-GNSA) for solving the II-RPDLB problem. The effectiveness of the proposed MIP-based bi-GNSA was verified by comparing its solutions with those obtained from the MIP model. Additionally, the improvement effect of the designed MIP-based bi-GNSA was verified with the original algorithm. The solution quality of the MIP-based bi-GNSA was validated with the NSGA-II and multi-objective enhanced differential evolution algorithm. Finally, a case study involving the disassembly of an EOL television was conducted to demonstrate the practical applicability of the bi-GNSA on an existing disassembly line.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101036"},"PeriodicalIF":10.4,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Information scene-augmented mapping for smart bearing whole life cycle digital twin","authors":"Zixian Li ,&nbsp;Hebin Zheng ,&nbsp;Shenlan Liu ,&nbsp;Wenbin Huang ,&nbsp;Xiaoxi Ding ,&nbsp;Xiaohui Chen","doi":"10.1016/j.jii.2025.101021","DOIUrl":"10.1016/j.jii.2025.101021","url":null,"abstract":"<div><div>Benefiting from the digitization of mechanical equipment, the digital twin of smart bearing can better realize the whole life cycle intelligent operation and maintenance of mechanical equipment, where the twin data are normally utilized to realize the state mapping with the identification or prediction model. Whereas, this process is mostly single interaction, and the dynamic update of the twin model and mapping results is not considered, and this makes its real application difficult. Focusing on this issue, an information scene-augmented mapping method (ISAM) is proposed for the smart bearing whole life cycle digital twin, so as to realize the accurate dynamic interaction of virtual-real scene in the twinning process. Different from the conventional digital twin models, ISAM creates an state mapping method that can dynamically update real state and simulation parameters, and it simultaneously enhances the scenario self-consistency ability based on information scene augment. First, a physical information and prior-knowledge driven feature parameter matching network (PK-FPMN) was constructed, and the actual fault size can be dynamically matched by the measured data and the dynamic model. This will realize the virtual-real scene interaction of the digital twin. Considering the difference between the twin data and the actual data, progressive style cyclic enhancement network (PSCEN) model is then introduced in the parameter matching process. By transferring the style information of the measured information scene to the twin data, the self-consistency ability of the method in different application scenarios is improved. Finally, ISAM combines the physical entity and dynamic model to form a whole life cycle digital twin of smart bearing. And the mapped degradation state and twin data can be operated for state identification and degradation prediction. Experimental results demonstrate that the ISAM can accurately map the actual degradation state and improve the quality of twin data based on the real information scene. With virtual scene and real scene interacted, the degradation state and twin data can be used for accurately state identification and degradation prediction. It can be foreseen that the proposed ISAM for smart bearing has the potential to realize the intelligent operation and maintenance of mechanical equipment in actual industrial digitization scenarios.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101021"},"PeriodicalIF":10.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145650849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}