Journal of Industrial Information Integration最新文献

{"title":"F2P-Net: A Hybrid Prompt-Enhanced Dual-Branch Cooperative Network for Industrial Surface Defect Segmentation with Limited Data","authors":"Kerong Yan ,&nbsp;Shuai Chen ,&nbsp;Min Xu ,&nbsp;Peiye Sun ,&nbsp;Rui Wang","doi":"10.1016/j.jii.2025.100986","DOIUrl":"10.1016/j.jii.2025.100986","url":null,"abstract":"<div><div>Industrial surface defect detection is constrained by the scarcity of defective samples and by the insufficient capacity of current segmentation methods to precisely delineate defect boundaries. To address these challenges, we propose <strong>F2P-Net</strong>, a few-sample, highly precise industrial surface defect segmentation framework composed of three core modules. <strong>ViCNet</strong> (ViT and CNN collaborative encoder network) integrates a vision transformer backbone with an auxiliary convolutional branch to retain robust large-model priors while enhancing sensitivity to fine-scale textures and local irregularities. <strong>AFDec</strong> (automated geometric prompt and multi-scale feature fusion decoder) employs automated geometric prompts to localize potential defect regions and fuses hierarchical multi-scale features to improve boundary delineation and mask consistency. <strong>EVPT</strong> (edge-enhanced visual prompt tuning) is a fine-tuning module incorporating edge-explicit visual prompt to facilitate effective industrial domain adaptation of large vision models. The proposed method achieves considerable performance over existing full-data training approaches in metrics including mAP, Recall, and IoU using only 1.76 %∼3.06 % of training images across NEU_Seg, MT, KolektorSDD2, and DAGM2007 datasets. Under full-data training, it attains state-of-the-art segmentation accuracies with IoU scores of 86.03 %, 92.57 %, 78.77 %, and 82.55 %, respectively. The network provides a novel solution for industrial applications with few-sample, high-precision defect segmentation. Code is available at <span><span>https://github.com/kerongYan/F2P-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"48 ","pages":"Article 100986"},"PeriodicalIF":10.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416549","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":"Residual group attention network with depthwise separable convolutional neural network for credit evaluation and early warning in finance","authors":"Jayaraman Kumarappan ,&nbsp;Ammupriya A ,&nbsp;S. Vijay Mallikraj ,&nbsp;Mohammed Al-Farouni","doi":"10.1016/j.jii.2025.101002","DOIUrl":"10.1016/j.jii.2025.101002","url":null,"abstract":"<div><div>The integration of big data technology with the Internet of Things (IoT) in finance presents an opportunity to enhance credit evaluation and early warning systems for credit risks. Traditional methods face challenges in processing multi-source heterogeneous financial data, leading to inaccuracies in credit risk assessments. This paper proposes an advanced approach using Local and Global Depth Normalization for data preprocessing, which enhances data quality and consistency. For feature extraction, a Spike-Driven Transformer effectively captures intricate patterns in financial transactions. A Residual Group Attention Network with Depthwise Separable Convolutional Neural Network (RGA-DSCNN) is then employed for classification, providing high accuracy in credit risk assessment by capturing both local and global feature dependencies. To further enhance the model's performance, the Mountaineering Team-Based Optimization technique is applied to optimize the parameters of the RGA-DSCNN. The proposed model is evaluated using IoT financial data consisting of 26 indicators, and factor analysis is conducted using SPSS26.0 software for initial validation. The results demonstrate that this method significantly outperforms existing techniques, achieving more precise credit risk assessments and reducing the probability of Type I and Type II errors in credit evaluation. This approach offers an effective and robust solution for banks, facilitating more accurate credit risk management, reducing potential losses, and improving profitability in IoT finance operations.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"48 ","pages":"Article 101002"},"PeriodicalIF":10.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145383755","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":"Sustainable building material supplier assessment in Pythagorean neutrosophic setting using ITARA and MACONT methods","authors":"Tapas Kumar Paul ,&nbsp;Madhumangal Pal","doi":"10.1016/j.jii.2025.101000","DOIUrl":"10.1016/j.jii.2025.101000","url":null,"abstract":"<div><div>Supplier selection plays a pivotal role in the construction industry, particularly for companies that seek to harmonize sustainability, innovation, and quality in their operations. This paper introduces a novel multi-criteria decision-making (MCDM) framework that combines the Indifference Threshold-Based Attribute Ratio Analysis (ITARA) and the Mixed Aggregation by Comprehensive Normalization Technique (MACONT) within a Pythagorean neutrosophic environment. The model is designed to assess suppliers using four critical dimensions: product performance, innovation in the supply chain, service effectiveness, and environmental sustainability, tailored to meet the strategic needs of a prominent Indian company. By utilizing Pythagorean neutrosophic sets, the proposed approach effectively addresses uncertainties and ambiguities in expert evaluations, resulting in a more adaptable and dependable decision-making process. The application of this hybrid ITARA-MACONT method shows its effectiveness in selecting top-performing suppliers who align with these multifaceted requirements, thereby enabling companies to make decisions that prioritize both sustainability and innovation. The practical application of this method illustrates the model’s feasibility and robustness. Moreover, the proposed framework supports industrial information integration by enabling the structured fusion of heterogeneous expert judgments and quantitative performance metrics, critical for informed decision-making in digital supply chains.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"48 ","pages":"Article 101000"},"PeriodicalIF":10.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145442110","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":"Task-level failure diagnosis in process control systems under cyberattack based on multilevel business process models","authors":"Xuqing Liang ,&nbsp;Chunjie Zhou ,&nbsp;Yu-Chu Tian ,&nbsp;Peihang Xu ,&nbsp;Minglu Wang ,&nbsp;Xin Du","doi":"10.1016/j.jii.2025.101001","DOIUrl":"10.1016/j.jii.2025.101001","url":null,"abstract":"<div><div>Ensuring the secure operation of process control systems (PCSs) is critical in the face of increasing cyberattacks. Diagnosing the causes of safety protection failures and predicting potential failure paths during cyberattacks can help maintain the system stability and security. However, cyberattacks often target not only assets but also control tasks or disrupt business processes, rendering existing asset-based failure diagnosis methods insufficient. To address this gap, this paper presents a novel task-level failure diagnosis approach based on multilevel business process models (BPMs) for PCSs. The model is constructed based on Business Process Model and Notation (BPMN) 2.0 by analyzing the interactions between devices, tasks, and business processes. It is then mapped to a multilevel Bayesian network (BN) for quantitative analysis under cyberattacks. Finally, the proposed approach is validated through a simulated distillation unit. Results show that cause diagnosis confirms sensing task failure (40.7%) as the main cause of business process failures, primarily due to data transmission failure (54.5%). The method effectively diagnoses failure causes at the task level and predicting potential system safety protection failure paths.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"48 ","pages":"Article 101001"},"PeriodicalIF":10.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145412200","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":"Exploring the relationships between formalisation and validation tools in sustainability assessment models: Insights from formal concept analysis","authors":"Sundeep Tamak ,&nbsp;Yasamin Eslami ,&nbsp;Nicolás Leutwyler ,&nbsp;Catherine Da Cunha","doi":"10.1016/j.jii.2025.100999","DOIUrl":"10.1016/j.jii.2025.100999","url":null,"abstract":"<div><div>Sustainability has emerged as a critical concern for manufacturing organisations due to increasing resource scarcity, alongside many other environmental and social concerns. Sustainability assessment models (SAM) play a vital role in evaluating and improving the environmental, economic, and social impacts of manufacturing organisations. To develop a robust sustainability assessment model, it is important to understand how these models are formalised and validated. Consequently, discovering the relationships, whether implicit or explicit, among the formalisation and validation tools can be of value. To this end, the present work uses Formal Concept Analysis, as a clustering tool, to uncover the hidden relationships among several SAM formalisation and validation tools. The findings, in terms of association rules, reveal common pairings of formalisation and validation tools. In addition, a Decision Support System (DSS) has been developed to further assist the researchers in the sustainability assessment field to identify complementary formalisation and validation tools. The DSS takes a formalisation tool as input and, leveraging the derived association rules, provides ranked recommendations for additional complementary formalisation and validation tools. This research contributes to the existing literature by bridging the gap in understanding the interactions among SAM formalisation and validation tools, ultimately leading to more reliable and effective sustainability assessments in manufacturing.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"48 ","pages":"Article 100999"},"PeriodicalIF":10.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145383756","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":"Intelligent prognostics of syngas pipeline elbow erosion via a hybrid machine learning–digital twin framework","authors":"Yangyang Bao ,&nbsp;Zeyang Shi ,&nbsp;Xian Li ,&nbsp;Yan An ,&nbsp;Wenming Song ,&nbsp;Yuge Li ,&nbsp;Wei Wu ,&nbsp;Liping Wei ,&nbsp;Yuan Yan ,&nbsp;Debiao Li","doi":"10.1016/j.jii.2025.101006","DOIUrl":"10.1016/j.jii.2025.101006","url":null,"abstract":"<div><div>In modern energy and process industries, the growing demand for continuous, safe, and reliable operation poses increasing challenges to the structural integrity of critical equipment. Erosion, caused by complex gas–solid interactions, is a typical degradation mechanism that affects the service life and operational stability of components such as elbows in syngas pipelines. However, existing monitoring and prediction methods often have limited spatiotemporal resolution, poor robustness, and weak real-time capability. These limitations make it difficult to accurately capture and predict erosion evolution under fluctuating operating conditions. To overcome these challenges, this study develops a digital twin–based framework for erosion monitoring, forecasting, and risk assessment (DT-FEMR) that combines physical constraints with data-driven modeling. The framework establishes a complete process from physical sensing to predictive maintenance. It consists of three core modules for erosion field reconstruction, future condition prediction, and lifetime evaluation. Through this hybrid physics–data design, DT-FEMR enables real-time visualization of erosion morphology, prediction of future gas velocity trends, and probabilistic assessment of remaining life and risk levels. The proposed framework offers a scalable and transferable approach for integrating multi-source data, physical simulations, and machine learning models. It enhances the interpretability, adaptability, and reliability of erosion analysis, providing a foundation for intelligent monitoring and predictive maintenance of critical industrial equipment.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"48 ","pages":"Article 101006"},"PeriodicalIF":10.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145442129","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 sustainable electric vehicle smart production with work-in-process inventory of outsourced spare parts","authors":"Biswajit Sarkar ,&nbsp;Rekha Guchhait ,&nbsp;Mitali Sarkar","doi":"10.1016/j.jii.2025.100998","DOIUrl":"10.1016/j.jii.2025.100998","url":null,"abstract":"<div><div>Electric vehicle production has recently gained popularity due to increasing emissions. The research on electric vehicle production concerning technical, economic, and environmental aspects is very less compared to the traditional vehicle. This research studies a mixed-type electric vehicle production system that produces spare parts and finally assembles all spare parts for the vehicle. The spare parts production combines in-line production with returnable items and outsourcing. An automated inspection for both spare parts and vehicles is included within the system. Two different types of machines work for the production process: Machine 1 for spare parts and Machine 2 for vehicles. As the basic purpose is to provide an ecofriendly logistics facility, the manufacturing company takes care of carbon emissions from the system, customer satisfaction, and the green quality of vehicles. Necessary and sufficient conditions of classical optimization find global optimum solutions. Results show that green technology and customer satisfaction are two important factors for vehicle production. Comparative discussions, sensitivity, and robust analysis are provided to validate the theoretical contributions. The proposed mixed-type production model earns 85.32% more profit than a traditional production model. The electric vehicle provides a 96% customer satisfaction with an increase of 68.97% profit without customer satisfaction.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"49 ","pages":"Article 100998"},"PeriodicalIF":10.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145424057","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 of applications of AI in monitoring, inspection, and maintenance of railway tracks","authors":"Amin Khajehdezfuly ,&nbsp;Hadi Azizipour ,&nbsp;Sakdirat Kaewunruen","doi":"10.1016/j.jii.2025.101005","DOIUrl":"10.1016/j.jii.2025.101005","url":null,"abstract":"<div><div>With the advancement of Artificial Intelligence (AI)-based methods and the establishment of diverse databases, significant research has been conducted on the application of AI in railway track monitoring, inspection and maintenance. Although several review studies exist in this field, each has been confined to a limited scope, focusing on specific data types, data collection methods, or AI-based techniques. To date, no comprehensive review has been published that encompasses all data types, data collection methods, and AI-based approaches to assess prior research holistically. This study aims to address this critical gap by covering both passenger and freight railway transport systems. Firstly, the available databases used for AI applications in railway track inspection and maintenance were categorized and reviewed, distinguishing between peer-reviewed and non-peer-reviewed sources. Secondly, this review introduces a novel three-level classification framework, based on data acquisition method (including track response methods, on-board data approaches, and remote data methods), target railway track component or feature, and input data type, to systematically organize and analyze 567 studies the field published between 2005 and 2025. The findings reveal that the majority of research in this field (88 %) is concentrated on on-board data methods. Approximately 90 % of these studies focus on railway track components, specifically their identification or damage detection. Among the track components, rails and fastening systems, being both critical and vulnerable, have been the primary focus of most research efforts. Image data emerges as the most prevalent and widely utilized data type in on-board data approaches for all railway track components. An in-depth gap analysis was conducted on the literature to identify the limitations of previous studies and outline a roadmap for future research and open directions from multiple perspectives. A comprehensive review of the literature indicates a pressing need for the development of AI-based methods capable of processing multiple data types simultaneously to identify both internal and external damages across all railway track components. The limited number of studies addressing the integration of multiple data types underscores the significant research opportunities in this area. This review not only synthesizes AI-based methods for railway track monitoring and maintenance but also highlights their role in advancing industrial information integration by enabling scalable and intelligent fusion of multi-source data for real-time decision-making.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"48 ","pages":"Article 101005"},"PeriodicalIF":10.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145424060","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":"Shared steering control framework based on visual-haptic compliance information for mitigating human–machine conflict","authors":"Xuyang Wang,&nbsp;Wanzhong Zhao,&nbsp;Chunyan Wang,&nbsp;Ziyu Zhang,&nbsp;Xiaochuan Zhou,&nbsp;Yukai Chu","doi":"10.1016/j.jii.2025.100989","DOIUrl":"10.1016/j.jii.2025.100989","url":null,"abstract":"<div><div>To mitigate the adverse impact of human–machine conflict on driving safety, this paper proposes an indirect shared steering control framework that leverages drivers’ visual and haptic compliance as multimodal embodied cues for adaptive authority allocation. A novel dual-modal compliance index is defined by fusing visual and haptic signals, and a real-time estimation system is implemented alongside a driving risk prediction module. These components serve as inputs to a parallel fuzzy inference mechanism for dynamic authority distribution. A human–machine mutual-adaptation robust control strategy is then developed based on a cybernetic driver model with coordinated visual-haptic feedback. Within an integrated driver-vehicle-road framework, a multiobjective robust tube model predictive controller is designed to jointly optimize conflict mitigation and path-tracking performance. Driver-in-the-loop experiments conducted on a four-degree-of-freedom motion platform demonstrate that the proposed framework effectively reduces human–machine conflict while enhancing vehicle stability and tracking accuracy. The results highlight the utility of multimodal compliance fusion as an embodied intelligence mechanism for adaptive shared control and suggest its broader applicability to complex human-machine systems. Moreover, the embodied-intelligence-based cooperative mechanism provides transferable insights for multimodal information fusion and adaptive collaboration in industrial information integration.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"48 ","pages":"Article 100989"},"PeriodicalIF":10.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145383858","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":"AI-based data-driven framework optimizing smart manufacturing in industrial systems","authors":"Mohammed Salem Basingab","doi":"10.1016/j.jii.2025.100996","DOIUrl":"10.1016/j.jii.2025.100996","url":null,"abstract":"<div><div>Integrating Agent-Based Modeling (ABM) with the Industrial Internet of Things (IIoT) is reshaping how industries manage complexity, automate decision-making, and improve operational intelligence. This study presents a data-centric ABM–IIoT framework that strengthens the efficiency and responsiveness of industrial systems, particularly in manufacturing and planning domains. The framework supports dynamic simulation and predictive maintenance while assisting decision-making by integrating real-time sensor data with AI-powered analytics. Experimental evaluation demonstrates consistent improvements, including approximately 20 % higher operational performance and a 15 % reduction in resource consumption, along with observable gains in decision-making accuracy and process efficiency compared with conventional IoT-based approaches. The framework also maintains stable operation under uncertainty, confirming its adaptability and reliability in dynamic industrial environments. A Monte Carlo-based sensitivity analysis was conducted under varied industrial workloads and uncertainty conditions to validate the robustness and flexibility of the proposed system. Comparative evaluations against AI-only and rule-based models indicate stronger adaptability and awareness of complex system interactions when using agent-based approaches. The architecture further enables distributed decision-making through intelligent agents that replicate human and machine behaviors in industrial ecosystems. This study contributes to industrial systems design by linking predictive analytics with system-level modeling, providing a practical framework tailored for Industry 4.0 applications. The results suggest that ABM–IIoT integration can enhance automation and resilience while supporting more reliable decision-making in smart manufacturing environments.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"48 ","pages":"Article 100996"},"PeriodicalIF":10.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382975","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}