Journal of Industrial Information Integration最新文献

{"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}

{"title":"Overall water effectiveness: A new lean indicator for digital evaluation of water efficiency in industrial processes","authors":"Marcello Braglia ,&nbsp;Mohamed Afy-Shararah ,&nbsp;Francesco Di Paco ,&nbsp;Roberto Gabbrielli ,&nbsp;Leonardo Marrazzini","doi":"10.1016/j.jii.2025.101031","DOIUrl":"10.1016/j.jii.2025.101031","url":null,"abstract":"<div><div>Water management is becoming an increasingly critical challenge for manufacturing industries due to growing environmental concerns, stricter regulatory requirements, and rising pressure from clients demanding more sustainable practices. Efficient and transparent use of water resources is no longer optional but a strategic necessity across industrial sectors. In this paper a new Lean performance indicator for evaluating water usage in industrial processes is presented. The proposed indicator, named Overall Water Effectiveness, aims to systematically assess industrial water performance by quantifying the gap between actual and ideal performance. It builds on the logic of Overall Equipment Effectiveness to identify water-related losses and support informed decision-making for continuous improvement while introducing a comprehensive industrial loss structure specifically designed for water use and consumption. Jointly, two key additional indicators are introduced: one measures how effectively the production process consumes input water, while the other evaluates the dependency on external water sources, taking into account the contributions of recycled and returned water. By translating high-level sustainability goals into actionable operational metrics, this new set of indicators enables the integration of water management into daily industrial operations through a practical, easy-to-use tool. The approach is applied in a major textile manufacturing company, demonstrating its practical utility in evaluating water use and consumption, identifying loss patterns, and leading the identification of improvement actions.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101031"},"PeriodicalIF":10.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658118","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":"Onboard camera-LiDAR deployment optimization: Towards applications for pavement distress detection with multi-sensor fusion","authors":"Ganghao Sun ,&nbsp;Ciyun Lin ,&nbsp;Bowen Gong ,&nbsp;Hongchao Liu","doi":"10.1016/j.jii.2025.101018","DOIUrl":"10.1016/j.jii.2025.101018","url":null,"abstract":"<div><div>Multi-sensor fusion has emerged as a solution for accuracy and cost-effective pavement distress detection. However, optimizing the deployment to utilize sensors’ diverse capabilities and enhance their effectiveness has not been thoroughly addressed. Therefore, this study proposed a novel framework for onboard camera-LiDAR deployment optimization. First, mathematical models were formulated to calculate the scanning area of different sensors, taking into account their inherent attributes and external factors. Then, the differential evolutionary algorithm was improved to solve the object function to maximize the overlap area and optimize the deployment parameters. Finally, simulations and field experiments were conducted to verify the reliability of the proposed model. Experimental results demonstrated that the method achieved mean relative errors of 6.80% and 6.89% for points number deviation, and 2.89% and 2.52% with overlap area deviations in simulation and field tests, respectively, which indicated the effective of the proposed method for detecting pavement distress.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"49 ","pages":"Article 101018"},"PeriodicalIF":10.4,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619784","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":"Leveraging model-based systems and software engineering for digital twin engineering: Methods and digital thread opportunities","authors":"Clarissa Gregory ,&nbsp;Souad Rabah ,&nbsp;Vincent Chapurlat ,&nbsp;Fedor Burčiar ,&nbsp;Monika Herchlová","doi":"10.1016/j.jii.2025.101023","DOIUrl":"10.1016/j.jii.2025.101023","url":null,"abstract":"<div><div>The Digital Twin (DT) is a reciprocally connected and synchronized representation of a physical asset so-called Physical Twin. DT is a central component of a Digital Twin System (DT System), that interconnects the DT and the Physical Twin, through different components, and executes the necessary functions such as exchanging, processing or storing data. The engineering of the DT System is a long and complex process which is poorly formalised by systematic methodological approaches despite the variety of techniques and technologies used by practitioners. Fittingly, Systems Engineering (SE) offers principles, processes, methodological tools and frameworks for the engineering and lifecycle management of complex systems. More precisely, Model-Based Systems and Software Engineering (MBSSE) places modelling activities at the centre of engineering practices to facilitate exchanges and iterations between stakeholders around the system of interest through its whole lifecycle. Stakeholders’ activities during system’s lifecycle produce digital items, perceived as Data, Information or Knowledge (DIK), which may be reused for DT System engineering. First, this paper discusses SE and MBSSE application for DT System engineering to identify the foundations of a DT System MBSSE. Second, a discussion about opportunities and research questions are presented to gather and manage items produced along lifecycle in a concept designated the Digital Thread. Indeed, Digital thread formalisation and exploitation lead various interests for DT System engineering.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101023"},"PeriodicalIF":10.4,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619782","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":"Emerging perspectives on embodied intelligence in future smart manufacturing","authors":"Dan Xia ,&nbsp;Pengpeng Xu ,&nbsp;Guangjie Han ,&nbsp;Jinfang Jiang","doi":"10.1016/j.jii.2025.101020","DOIUrl":"10.1016/j.jii.2025.101020","url":null,"abstract":"<div><div>Embodied intelligence has emerged as a transformative paradigm in artificial intelligence, representing the convergence of multimodal perception, cognitive reasoning, and physical interaction with the environment. In the context of smart manufacturing, it is increasingly recognized as a key enabler for future intelligent systems capable of adapting to dynamic, unstructured, and human-centric production environments. With the rapid development of large multimodal models, embodied intelligence is poised to achieve unprecedented levels of generalization, autonomy, and task versatility through continuous learning and real-world interaction. Therefore, this article conducts a systematic review of the current research status and development trends of embodied intelligence in smart manufacturing, analyzes its key technologies, summarizes typical application scenarios, and further discusses the challenges and future research directions, aiming to provide new insights and guidance for smart manufacturing driven by embodied intelligence.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101020"},"PeriodicalIF":10.4,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619783","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":"Cognitive-based framework for detecting and diagnosing broken bars in induction motors for industry maintenance","authors":"Narco Afonso Ravazzoli Maciejewski ,&nbsp;Roberto Zanetti Freire ,&nbsp;Anderson Luis Szejka ,&nbsp;Thiago de Paula Machado Bazzo ,&nbsp;Sofia Moreira de Andrade Lopes ,&nbsp;Rogério Andrade Flauzino","doi":"10.1016/j.jii.2025.101022","DOIUrl":"10.1016/j.jii.2025.101022","url":null,"abstract":"<div><div>Three-phase induction motors are the primary actuators for converting electrical energy into mechanical energy in the productive sector, constituting key assets due to their widespread use and critical function. Reducing maintenance costs and implementing predictive techniques incentivize the development of systems to identify intrinsic defects. The increasing demand for customization in manufacturing affects maintenance due to fast production line adaptations. This leads to unforeseen failures that compromise reliability. There is a lack of research on detecting and diagnosing faults in induction motors under intermittent drives or varying operating conditions. To fill this gap, the present research proposes a methodology for recommending algorithms to diagnose and detect broken bar defects in three-phase induction motors during transient operation based on a cognitive system. The framework explains and detects fault causality. Using experimental data (current, voltage, vibration), three-phase induction motors were tested under normal conditions, applying various severities of broken bar faults with load torque variations. Features were extracted from each signal, and feature selection algorithms of different mathematical natures were applied. Machine learning models were built, validated, and tested with multicriteria measures. To assess robustness, white noise was inserted into the experimental signals. The Consistency-Based Filter algorithm emerged as the most suitable for feature selection combined with Random Forest and Multilayer Perceptron models. The best results were achieved with up to 80 % noise tolerance without compromising predictive capacity for diagnosing defect severity. Features following a Gaussian distribution showed better predictive capacity, resulting in a reliable framework for fault diagnosis in induction motors.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101022"},"PeriodicalIF":10.4,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145611921","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":"The interplay of data-driven insights and AI anxiety in shaping the impact of AI capabilities on circular economy capability","authors":"Robinson Garcés-Marín ,&nbsp;José Arias-Pérez ,&nbsp;Camilo Restrepo-Estrada","doi":"10.1016/j.jii.2025.101019","DOIUrl":"10.1016/j.jii.2025.101019","url":null,"abstract":"<div><div>In a world facing pressing environmental challenges like climate change and resource scarcity, Artificial Intelligence (AI) is widely regarded as a powerful tool to enhance and support sustainability goals via Circular Economy Capability (CEC). The organizational capacity to leverage this technology, Artificial Intelligence Capability (AIC), is conceptualized through the lens of the Resource-Based Theory (RBT) as the capacity to effectively implement and utilize AI to generate strategic value. However, the direct relationship between AIC and CEC is not straightforward. The purpose of this research is to investigate this nuanced relationship by examining how socio-technical factors such as Data-Driven Insights (DDI)—actionable inferences derived from analytics over data—and AI Anxiety—stemming from employees' fear of job loss—shape the relationship between AIC and CEC. Using a moderated mediation model and Partial Least Squares Structural Equation Modeling (PLS-SEM), we analyzed data from firms with moderate to high technology maturity. While the study’s results are primarily based on context-specific evidence, which invites further investigation into generalizability to other settings, our findings suggest that the direct effect of AIC on CEC is not significant. Instead, DDI significantly mediate the relationship, confirming that AIC must be bundled with actionable insights to create value. Crucially, AI anxiety negatively moderates the effect of DDI on CEC. This means that while organizations may generate valuable insights, employee resistance and fear hinder their effective translation into sustainability practices. This study highlights the critical socio-technical barriers to AI adoption and their impact on achieving sustainability goals.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"49 ","pages":"Article 101019"},"PeriodicalIF":10.4,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145593631","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":"Harnessing collective intelligence of multi-agent LLM systems for sensor failure reasoning in smart manufacturing","authors":"Wei Gong ,&nbsp;Shuang Qiao ,&nbsp;Chenhong Cao ,&nbsp;Shilei Tan ,&nbsp;Junliang Ye ,&nbsp;Haoxiang Liu ,&nbsp;Si Chen ,&nbsp;Xuesong Wang","doi":"10.1016/j.jii.2025.101012","DOIUrl":"10.1016/j.jii.2025.101012","url":null,"abstract":"<div><div>In smart manufacturing, accurate sensor fault diagnosis is essential for operational integrity. However, the direct application of Large Language Models (LLMs) to this task yields unstructured analyses and inefficient resource use. To address these challenges, we propose a novel multi-agent framework that instills a structured, modular, and adaptive reasoning process. The framework features a Reasoning Module to classify problem complexity and a Decision Module that employs a difficulty-aware workflow. Simple problems are resolved directly, while complex cases activate a deliberative debate among multiple agents to form a consensus. Evaluated on the specialized FailureSensorIQ benchmark, our framework significantly boosts the performance of open-source LLMs. For example, Llama3.1-8B-instruct’s accuracy surged from 36.5% to 54.6%—an 18.1 percentage point improvement. Crucially, our method empowers smaller 7B/8B models to surpass larger, proprietary models like GPT-4o-mini. Ablation studies validate that our dynamic routing mechanism provides an optimal trade-off between diagnostic accuracy and computational cost. This work establishes a new paradigm for industrial fault diagnosis, improving accuracy, interpretability, and resource efficiency, thereby paving the way for reliable and accessible AI in critical manufacturing systems.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"49 ","pages":"Article 101012"},"PeriodicalIF":10.4,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567554","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":"An anonymization framework for IEC 61850 substation communications: Field-level and topology-aware privacy","authors":"Soheil Shirvani ,&nbsp;Emmanuel D. Buedi,&nbsp;Kwasi Boakye-Boateng,&nbsp;Yoonjib Kim,&nbsp;Rongxing Lu ,&nbsp;Ali A. Ghorbani","doi":"10.1016/j.jii.2025.101013","DOIUrl":"10.1016/j.jii.2025.101013","url":null,"abstract":"<div><div>Substation datasets, like those using the IEC61850 standard, hold sensitive information about power flows, equipment statuses, and network configurations. This data could expose vulnerabilities to knowledge-based cyberattacks, making utility providers hesitant to share it publicly for research. While encryption enhances security, it often diminishes the dataset’s utility for research purposes. To address the trade-off between security and utility, we introduce an anonymization technique specifically for the IEC61850 standard, demonstrated on the GOOSE protocol. Our method involves two main approaches: anonymizing sensitive and quasi-identifying fields within packets to preserve data utility, and injecting dummy packets using one of our proposed algorithms to effectively obscure network topology. Using the first method, we publish an anonymized dataset derived from substation communications captured in our testbed to support ongoing research. We evaluated the framework’s effectiveness through a comprehensive communication pattern analysis, including time, flow, statistical, and entropy analyses, and field anonymization testing. Our study highlights the critical importance of maintaining privacy in substation data sharing while ensuring data remains useful for research, setting the foundation for extending this framework across multiple substation protocols in future studies.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"49 ","pages":"Article 101013"},"PeriodicalIF":10.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145554067","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":"LLM-MTMP: A large language model-based multi-agent task and motion planning framework for power inspection robots","authors":"Zongyuan Wang ,&nbsp;Xin Zhou ,&nbsp;Jianliang Mao ,&nbsp;Chuanlin Zhang ,&nbsp;Chenggang Cui ,&nbsp;Jun Yang","doi":"10.1016/j.jii.2025.101014","DOIUrl":"10.1016/j.jii.2025.101014","url":null,"abstract":"<div><div>Manually designing robotic task sequences is labor intensive and inefficient, especially in power inspection tasks that involve academic background knowledge and complex operation rules. To overcome this limitation, this paper presents a large language model-based multi-agent task and motion planning framework, LLM-MTMP, to enable autonomous human–robot interaction and task execution of robot in power inspection scenarios. It combines enhanced resource generation technology with a specific knowledge base in the field of power inspection, converting and decomposing natural language into a set of operation sequences that are readable by robots, thereby enabling autonomous inspection operations that meet specific industrial requirements. Experimental results from physical deployments on robotic platforms demonstrate that LLM-MTMP significantly improves task generation success rates and expands operational adaptability compared to baseline methods, highlighting its practical value for industrial applications.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"49 ","pages":"Article 101014"},"PeriodicalIF":10.4,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145554068","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}