Journal of Industrial Information Integration最新文献

{"title":"Federated split learning-driven multimodal physical-virtual integration framework: high-fidelity full-cross-section deformation field reconstruction in precise metal tube bending manufacturing","authors":"Zili Wang ,&nbsp;Xinlei Hu ,&nbsp;Shuyou Zhang ,&nbsp;Lemiao Qiu ,&nbsp;Yaochen Lin ,&nbsp;Liangyou Li ,&nbsp;Yongzhe Xiang ,&nbsp;Jie Li","doi":"10.1016/j.jii.2025.101048","DOIUrl":"10.1016/j.jii.2025.101048","url":null,"abstract":"<div><div>During the metal tube bending (MTB) process, high-fidelity reconstruction of full cross-section (FCS) deformation is critical to the robustness of closed-loop control in tube-bending manufacturing systems. However, the distributed nature of industrial data, the spatiotemporal discontinuity of physical sensing, and the heterogeneity of multimodal physical–virtual data hinder effective integration of distributed sources and precise reconstruction of the transient deformation of tube surfaces. To address these challenges, we propose a Federated Split-Learning–Driven Multimodal Physical–Virtual Integration (FSLD-MPVI) framework. Leveraging a hybrid distributed–centralized architecture with cross-level collaborative fusion, FSLD-MPVI enables efficient integration and knowledge sharing of local high-fidelity visual data, global low-fidelity finite-element (FE) simulation data, and static process parameters that are dispersed across manufacturing nodes. Within the split learning (SL) distributed architecture, three cascaded, heterogeneous subnetworks are deployed, each dedicated to fusing a specific class of hybrid modality inputs, thereby providing the infrastructure needed to integrate modalities originating from different workshops. In the federated learning (FL) layer, a centralized server aggregates the parameters of each subnetwork respectively, mitigating cross-node data isolation while preserving data locality. Experiments demonstrate that FSLD-MPVI achieves high-accuracy global reconstruction (R² = 0.9973); in the 90° bending case, the shape deviation remains within 0.2 mm. These results verify that multimodal physical–virtual integration strongly supports precise global reconstruction of FCS deformation fields and establishes a new paradigm for intelligent process monitoring in advanced manufacturing systems.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101048"},"PeriodicalIF":10.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845121","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":"Mixed objective scheduling optimization in mountain orchards under energy-saving for carbon neutrality","authors":"Zhentao Xue ,&nbsp;Zhigang Ren ,&nbsp;Jian Chen ,&nbsp;Xiqing Wang ,&nbsp;Shuaisong Zhang","doi":"10.1016/j.jii.2025.101049","DOIUrl":"10.1016/j.jii.2025.101049","url":null,"abstract":"<div><div>The air-ground cooperative plant protection unmanned formation can effectively deal with the complex terrain challenges of mountain orchards and ensure the uniformity of plant protection operation coverage. The core of this system lies in the principles of Industrial Information Integration Engineering (IIIE). Through dynamic scheduling optimization, it can alleviate the problems of large energy consumption and long non-operation paths. Aiming at the dynamic scheduling planning problem, this study proposes an energy-saving hybrid target scheduling optimization method based on an improved Australian wild dog hunting strategy. A novel mountain orchard path coding technology is designed, and an energy consumption model based on the principle of unmanned formation dynamics is established, which provides a scientific basis for formulating efficient energy-saving strategies. The improved Australian wild dog hunting strategy combines the motion constraints of unmanned formation and the requirements of plant protection tasks, and realizes the efficient optimization of the scheduling scheme. Numerical experiments demonstrated the effectiveness of the proposed method, which reduced the objective function to 65.63% of the initial solution in simulations, outperforming the genetic algorithm. This performance was further validated in a real-world scenario, where the value was reduced to 57.34%. This efficient dynamic scheduling optimization serves as a key enabler for agricultural industry integration and informatization.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101049"},"PeriodicalIF":10.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845508","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":"2026-03-01","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":"Implementing TinyML in Internet of Things devices: A systematic literature review","authors":"Andrés Felipe Solis Pino ,&nbsp;Daniel Steven Moran Pizarro ,&nbsp;Pablo H. Ruiz ,&nbsp;Vanessa Agredo-Delgado ,&nbsp;Cesar Alberto Collazos ,&nbsp;Fernando Moreira","doi":"10.1016/j.jii.2026.101065","DOIUrl":"10.1016/j.jii.2026.101065","url":null,"abstract":"<div><div>The Internet of Things is at the heart of society and is experiencing rapid expansion. Its integration with Artificial Intelligence and Machine Learning has led to the emergence of Tiny Machine Learning (TinyML), which enables data processing directly on the device, improving efficiency, reducing latency, and increasing data privacy. Despite the growing relevance of TinyML in the Internet of Things, there is a lack of systematic literature reviews providing a holistic understanding of its implementation, advances, and challenges, which hinders a clear understanding of the available empirical evidence and best practices. To bridge this gap, this study presents a systematic literature review, adhering to the PRISMA protocol and employing a multi-database search strategy, identifying 114 primary studies. The review reveals that TinyML is consolidating as a transformative paradigm for the Internet of Things, experiencing significant research growth since 2020. Applications are diverse, with healthcare and environmental monitoring being the most notable examples. Deep learning models, particularly convolutional neural networks, are frequently employed in this context. The main challenges identified include security vulnerabilities, the need to address ethical considerations like algorithmic bias, and hardware limitations related to memory and processing power. Ultimately, this review offers valuable insights into the current state and prospects of TinyML in the Internet of Things, providing a valuable resource for researchers, developers, and decision-makers in this rapidly evolving field.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101065"},"PeriodicalIF":10.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957253","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 review on machine learning and deep learning techniques for plant leaf disease detection and classification with IoT in agriculture industry","authors":"Priyadharshini Arputharaj,&nbsp;Kalaivanan Karunanithy","doi":"10.1016/j.jii.2026.101078","DOIUrl":"10.1016/j.jii.2026.101078","url":null,"abstract":"<div><div>Agriculture serves as a major source of food and plays a key function as the backbone of most countries’ economies. However, farmers are encountering many challenges in this sector, such as drought, flooding, diseases, nutrient deficiency, and so on. The technological advancements in the field of agriculture, also called smart agriculture, are necessary to address the requirements of the expanding population and manage the associated challenges. Among those, plant leaf diseases are the primary concern that severely impacts crop yield and economic stability. This technical review examines various Machine Learning (ML) and Deep Learning (DL) approaches used to identify and classify different plant leaf diseases. This review gives an overview of the current state-of-the-art ML, DL, and IoT-enabled disease prediction systems and their recent advances in developing an intelligent system in smart agriculture. It provides insights into the various technological developments and discusses the benefits and opportunities of AI-based models in plant disease management.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101078"},"PeriodicalIF":10.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033487","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-layered data service model for Cyber-Physical Production Networks","authors":"Ada Bagozi,&nbsp;Devis Bianchini,&nbsp;Massimiliano Garda,&nbsp;Michele Melchiori,&nbsp;Anisa Rula","doi":"10.1016/j.jii.2025.101043","DOIUrl":"10.1016/j.jii.2025.101043","url":null,"abstract":"<div><div>In modern smart factories, supply chains are no longer isolated; instead, they are evolving into interconnected and dynamic networks, where intertwined supply chains enable real-time collaboration and data sharing for adaptive decision-making across multiple stakeholders. By harnessing data from sensors and connected devices, data-driven decisions can be made to optimize the entire supply chain, and to provide novel and customer-friendly products and services. Cyber-Physical Systems form the foundation of Cyber-Physical Production Systems (CPPS) by enabling real-time data exchange and intelligent automation at the factory level, while horizontal integration connects CPPS across different production facilities to enhance supply chain coordination, thus forming the so-called Cyber-Physical Production Networks (CPPN). In CPPN, the Internet of Services (IoS) paradigm, in combination with the Internet of Things (IoT), plays a crucial role in facilitating horizontal integration and seamless collaboration between intertwined supply chains. Since the IoS paradigm has to enable data sharing and processing within individual smart factories and across factory borders, there is a need to design service-oriented architectures specifically tailored to data governance in both CPPS and CPPN. However, existing service-oriented approaches for CPPS primarily focus on deployment layers (e.g., fog/edge computing or IT/production levels) while neglecting data-oriented aspects, limiting modularity and effective data service design across CPPS and CPPN levels. To bridge this gap, in this paper, we propose a multi-layered service-oriented model for CPPN focused on data services, which includes atomic services for data collection and processing, and composite services for governing the data flow within smart factories and throughout the supply chains they participate in. One of the significant advantages of the multi-layered approach is a clear separation of concerns in service design, with the ability to address issues of modularity, scalability, data sovereignty and data access, by distinguishing between CPPS and CPPN levels. In the paper, we critically evaluate different strategies for the management of a service ecosystem that is compliant with the proposed model.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101043"},"PeriodicalIF":10.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883956","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":"Multi-source domain adaptation via evidence-based target pseudo-labels for human–computer collaboration fault diagnosis","authors":"Jilun Tian ,&nbsp;Hao Luo ,&nbsp;Pengfei Yan ,&nbsp;Xinyu Qiao ,&nbsp;Shimeng Wu ,&nbsp;Jiusi Zhang","doi":"10.1016/j.jii.2026.101077","DOIUrl":"10.1016/j.jii.2026.101077","url":null,"abstract":"<div><div>Existing data-driven fault diagnosis methods imply the decision-making automatically, but lack adaptation and trustworthiness for varying working conditions. Unsupervised domain adaptation (UDA) relies on the cross-domain distribution disparity to achieve high-performance diagnostics. However, it struggles in complex multi-domain and diverse-source scenarios, which currently lack in-depth analysis. The proposed approach implements a novel multi-source domain adversarial network (MSDA) architecture via evidence-based target pseudo-label learning (ETPL) with dynamic multi-loss weightings. Specifically, MSDA constrains the disparity of diverse source–target pairs to obtain generalized domain-invariant features via an adversarial mechanism, and ETPL performs target pseudo-label learning while applying Dempster–Shafer (DS) evidence theory to assign sample-wise weights through MSDA and an unsupervised algorithm. Meanwhile, this study provides a theoretical analysis including a detailed generalization error bound for multi-source scenarios and target pseudo-labels, illustrating its dependence on distribution discrepancy and pseudo-label quality metrics. Human–computer collaboration approach is adopted to strengthen both advantages from human and machines by sample-wise analysis. Sufficient experimental results on two real-world case studies validate the effectiveness, successfully accomplishing complex cross-domain fault diagnosis and illustrating its potential applications in industrial settings.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101077"},"PeriodicalIF":10.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071634","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 knowledge-driven decision support architecture for sustainable supplier analysis in an infrastructure project","authors":"Song-Shun Lin ,&nbsp;Xin-Jiang Zheng ,&nbsp;Zhao-Yao Bao","doi":"10.1016/j.jii.2025.100994","DOIUrl":"10.1016/j.jii.2025.100994","url":null,"abstract":"<div><div>As supplier analysis becomes increasingly complex, there is a growing need for structured methods that support multi-dimensional evaluation under uncertainty. A knowledge-driven decision support approach (KDDSA) is proposed, leveraging entropy-based interval-valued spherical fuzzy sets to assign criteria weights. Additionally, a weighted coefficient of variation is introduced to measure consensus and account for variability in judgments, strengthening decision-making reliability. The proposed approach addresses the practical challenges of integrating multiple, often conflicting criteria in supplier analysis by incorporating economic, environmental, social, and supplier-specific dimensions, structured across sixteen indicators. To assess its practical applicability, KDDSA is applied to an infrastructure project, where uncertain assessments are integrated and processed through a multi-layered decision structure. The results highlight the critical importance of consensus building and uncertainty management for achieving reliable outcomes. By integrating heterogeneous information with advanced fuzzy modeling, the proposed approach enhances industrial information integration in complex decision-making contexts. The findings reinforce the potential of structured and information-integrated evaluation methods in enhancing supplier management within infrastructure supply chains.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 100994"},"PeriodicalIF":10.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382969","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":"Ensemble-based ship weather multi-objective route optimization","authors":"Kumars Mahmoodi ,&nbsp;Jari Böling ,&nbsp;Roberto Vettor","doi":"10.1016/j.jii.2026.101075","DOIUrl":"10.1016/j.jii.2026.101075","url":null,"abstract":"<div><div>Many traditional and state-of-the-art ship routing methods rely on single-objective formulations, deterministic weather inputs, or fixed operational assumptions, which may lead to suboptimal or impractical routing decisions under realistic and uncertain marine environments. This study presents an ensemble-based multi-objective optimization framework for ship route planning under uncertain weather conditions. The framework integrates a neural network model, trained on real onboard ship performance data and tuned using Bayesian hyperparameter optimization, to predict fuel consumption based on ship speed and marine weather parameters. An ensemble of weather forecasts is assigned to route waypoints using a bootstrapping method, enabling the evaluation of multiple cost functions reflecting trade-offs between voyage time, fuel consumption, and safety. Four optimization objective strategies — ensemble mean, worst-case, risk-aware, and Conditional Value-at-Risk (CVaR) — are implemented within a Grey Wolf Optimizer (GWO) to derive optimal routes across various voyages. The results demonstrate notable variations in route performance based on the selected strategy. For example, the CVaR approach achieves a balance between robustness and efficiency, with voyage fuel consumption for the longest journey (Voyage 3) reaching 490,475 kg, while the worst-case strategy prioritizes risk-averse paths, resulting in the highest fuel usage at 505,308 kg. Conversely, the ensemble mean strategy offers the lowest average fuel consumption (474,078 kg) but may expose the voyage to higher uncertainty. Furthermore, the proposed GWO demonstrates high precision in schedule adherence, maintaining arrival time deviations within a 30-minute margin across all optimized voyages, thereby justifying its effectiveness in handling complex multi-objective constraints. The developed framework is applicable to real-time voyage optimization and can support ship operators in achieving fuel efficiency and safety under varying ocean conditions.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101075"},"PeriodicalIF":10.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995691","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":"2026-03-01","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}