Computers in Industry最新文献

{"title":"A decentralised approach to cyber-physical systems as a service: Managing shared access worldwide through blockchain standards","authors":"Juan Luis Ramos Villalon ,&nbsp;Luis de la Torre ,&nbsp;Zhongcheng Lei ,&nbsp;Wenshan Hu ,&nbsp;Hugo Tadashi Kussaba ,&nbsp;Victoria Lemieux","doi":"10.1016/j.compind.2025.104264","DOIUrl":"10.1016/j.compind.2025.104264","url":null,"abstract":"<div><div>Cyber-physical systems (CPSs) is a general concept that encompasses a wide variety of systems. Depending on their nature, application, and accessibility needs and restrictions, CPSs can differ a lot from each other. This paper proposes a classification of CPSs based on their accessibility needs and restrictions and, more importantly, presents an approach to create a decentralised and worldwide common access management framework for CPSs for non-critical infrastructures that are meant to be shared and accessed remotely (i.e., offered as a service). The presented solution uses a permissionless blockchain, existing fungible tokens, and a combination of smart contracts based on nonfungible token standards/proposals to enable CPS owners to manage secure, flexible access without centralised oversight. In addition, the proposed framework provides built-in mechanisms for: (i) charging for the use of CPSs, (ii) availability calendar configuration, (iii) worldwide visibility, (iv) easy integration with authentication/authorisation methods, and (v) access control flexibility.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"167 ","pages":"Article 104264"},"PeriodicalIF":8.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428034","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":"On design of cognitive situation-adaptive autonomous mobile robotic applications","authors":"Daniel Pakkala,&nbsp;Niko Känsäkoski,&nbsp;Tapio Heikkilä,&nbsp;Jere Backman,&nbsp;Pekka Pääkkönen","doi":"10.1016/j.compind.2025.104263","DOIUrl":"10.1016/j.compind.2025.104263","url":null,"abstract":"<div><div>Fostered by the recent development in artificial intelligence technologies, digitalization in industries is proceeding towards intelligent automation of various physical work processes with autonomous robotic applications, in dynamic and non-deterministic environments, and in collaboration with human workers. The article presents an explorative case study on designing a cognitive situation-adaptive Autonomous Mobile Robotics (AMR) application for material hauling, in a simulated underground mining context. The goal of the research is to synthesize and present new design knowledge for improving situation-adaptation capabilities of AMR applications, which are increasingly required as the operational environments for the AMRs become dynamic, non-deterministic, and include people working on the same area with the robots. The research applies design science research methodology, and evaluates the results empirically via a prototype system, which is demonstrated in laboratory setting simulating an underground tunnel network. As an outstanding contribution, the results contribute a novel, nascent, and empirically evaluated design approach, which proposes three design aspects combining design and engineering activities across the systems engineering, knowledge engineering, computer science and robotics disciplines. Empirical evaluation is made via design, development, and demonstration of a system architecture and prototype system of a cognitive situation-adaptive AMR application, which is used in synthesis and evaluation of the design approach. The three design aspects proposed by the approach are 1) Context of operation, 2) Knowledge-driven behaviour, and 3) Knowledge driven operation. Also design challenges, future research and development needs, and innovation potential on designing of cognitive situation-adaptive AMR applications for industrial use are identified and discussed.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"167 ","pages":"Article 104263"},"PeriodicalIF":8.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive fault diagnosis of machining processes enabled by hybrid deep learning and incremental transfer learning","authors":"Yuchen Liang ,&nbsp;Yuqi Wang ,&nbsp;Weidong Li ,&nbsp;Duc Truong Pham ,&nbsp;Jinzhong Lu","doi":"10.1016/j.compind.2025.104262","DOIUrl":"10.1016/j.compind.2025.104262","url":null,"abstract":"<div><div>Faults occurring during machining processes can severely impact productivity and product quality. Deep learning models have been actively used to develop fault diagnosis approaches. However, it is challenging for industries to adopt the approaches due to their inability to adapt to varying machining conditions. To address the issue, a novel diagnostic approach is designed based on a hybrid convolutional neural network (CNN)-long short-term memory (LSTM) model and an incremental transfer learning strategy. Based on the incremental transfer learning, the CNN-LSTM model can acquire knowledge from previous machining conditions (source domain) and effectively apply it to new conditions (target domain). In the diagnostic approach, instance-based transfer learning, knowledge-based transfer learning, and incremental transfer learning are combined to improve the training efficiency and overcome the issue of forgetting previously learned knowledge. The CNN-LSTM-attention model is designed as a supplementary model when the data complexity is high. Experimental results show that the approach increased the average training accuracy from 88.63 % to 97.10 %, and required training datasets were reduced by 96.97 %. In addition, the incremental transfer learning reduced false detections for 71.24 %.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"167 ","pages":"Article 104262"},"PeriodicalIF":8.2,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419124","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 methodology for adaptive AI-based causal control: Toward an autonomous factory in solder paste printing","authors":"Marvin Herchenbach ,&nbsp;Sven Weinzierl ,&nbsp;Sandra Zilker ,&nbsp;Erik Schwulera ,&nbsp;Martin Matzner","doi":"10.1016/j.compind.2025.104256","DOIUrl":"10.1016/j.compind.2025.104256","url":null,"abstract":"<div><div>In recent years, there has been a remarkable shift from automated plants to intelligent production in the industrial context, accelerated by technologies such as artificial intelligence (AI). The ultimate goal is an autonomous plant that is capable of self-regulation and self-optimization. In electronics production, the first approaches have been proposed for deriving and adjusting machine parameters for solder paste printing in the surface-mount technology production of printed circuit boards. However, these approaches are often static and perform reactive actions since they are either based on expert systems or data-driven models. To reach a dynamic optimization, this work proposes a methodology, called adaptive AI-based causal control, allowing offline and online optimization. Following the principles of the Design for Six Sigma method, customer-oriented key performance indicators were derived, that aimed at a stable soldering process by focusing on the spread of the solder volume and a dedicated overall spread metric. The offline optimization (open-loop control) is based on a surrogate model approach to find optimal initial printing parameters. The online optimization (closed-loop control) employs a data-driven model predictive control to adjust the printing parameters dynamically. In addition, to consider the causal effects of the control variables in the online optimization, a causal graph is exploited in the predictive controller. Regarding the effectiveness of the open-loop control, our evaluation reveals a reduction in spread by 11.3% in production. Furthermore, in terms of the efficacy of the closed-loop control, we obtain a reduction in volume range by 16.7% in a simulated setting of the predictive controller. Thereby, the integration of a causal inference component based on a generated causal graph, achieving a recall of 76.9% by considering process knowledge identified with domain experts, accounts for about 2.8% of the recall.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"167 ","pages":"Article 104256"},"PeriodicalIF":8.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contribution to estimating the level of bearing degradation using a Multi-Branch Hidden Markov Model approach","authors":"Indrawata Wardhana ,&nbsp;Amal Gouiaa-Mtibaa ,&nbsp;Pascal Vrignat ,&nbsp;Frédéric Kratz","doi":"10.1016/j.compind.2025.104254","DOIUrl":"10.1016/j.compind.2025.104254","url":null,"abstract":"<div><div>The degradation of industrial systems is a natural and often unavoidable process. Hidden Markov Models (HMMs) are used for state-based bearing degradation analysis. A challenge arises because bearings can deteriorate in multiple ways, depending on crack locations. To address this, a Multi-Branch Hidden Markov Model (MB-HMM) was developed to handle multiple deteriorations. However, MB-HMM primarily uses simulated data where deterioration is known in advance. In contrast, real-world sensors collect data with uncertainties, potentially causing false alarms and impacting the First Predicting Time (FPT). We used the FEMTO-bearing dataset, which includes continuous monitoring until failure, with unknown fault locations and varying degradation levels. This study presents a comprehensive preprocessing framework and employs the Extended Multi-Branch HMM (EMB-HMM). Our experimental analysis shows that the proposed strategy significantly enhances the Signal-to-Noise Ratio (SNR). The active branch is defined based on prior and posterior probabilities, with the branch's prior probability and topology linked to the four fault frequencies of the bearing. The EMB-HMM outperforms other models in state prediction, featuring four branches and five hidden states. It improves state sequence accuracy, predicts degradation levels and FPT, and achieves zero false alarms for Fake Fault (FF).</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"167 ","pages":"Article 104254"},"PeriodicalIF":8.2,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349239","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":"Measure2Shape: A novel footwear customisation framework utilising 3D shape estimation from anthropometric measurements with an orthosis case study","authors":"Zhaohua Zhu ,&nbsp;Wenxuan Ji ,&nbsp;Yadie Yang ,&nbsp;Sio-Kei Im ,&nbsp;Jie Zhang","doi":"10.1016/j.compind.2025.104257","DOIUrl":"10.1016/j.compind.2025.104257","url":null,"abstract":"<div><div>To address the limitations of relying on expensive 3D scanners for obtaining foot data in footwear customisation, this paper introduces a novel framework, Measure2Shape, which estimates 3D foot shapes using anthropometric measurement data. To achieve this, we established a large-scale 3D foot dataset with measurement data and created statistical shape models (SSMs) to represent the range of foot variations. We then proposed efficient forward- and backward-search algorithms to accurately determine the regression matrix, which connects the optimal combination of 3D measurements to the SSM coefficients of the 3D foot shape. Compared to existing 3D foot model estimation methods, our approach achieves high-precision 3D foot shape predictions using fewer dimensional measurements, with the optimal number being 6 and an average prediction error of 2.49 (±0.75) mm. Additionally, orthosis designed based on the predicted 3D foot model effectively reduce both static and dynamic peak plantar pressures, validating the reliability of our model. More importantly, the proposed regression search method can be extended to 3D estimations for other body regions, offering a wide range of customisation solutions beyond footwear. In the future, we will further expand the dataset to build a more robust 3D foot prediction model. Our project will be publicly available at: <span><span>https://github.com/Easy-Shu/Measure2Shape</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"167 ","pages":"Article 104257"},"PeriodicalIF":8.2,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125022","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 immersive spatially consistent multi-modal augmented virtuality human-machine interface for telerobotic systems","authors":"Rebecca Schwenk,&nbsp;Shana Smith","doi":"10.1016/j.compind.2025.104260","DOIUrl":"10.1016/j.compind.2025.104260","url":null,"abstract":"<div><div>This study presents an immersive augmented virtuality (AV)-based human-machine interface (HMI) designed to enhance telepresence and operator performance in telerobotic systems. Traditional telerobotic systems often face limitations such as 2D representations of 3D environments, restricted fields of view, and reduced depth perception, all of which hinder operator effectiveness. Although extended reality and various augmentation technologies have been employed to create more intuitive teleoperations, prior research has largely overlooked the integration of spatially consistent video streams from remote sites, which significantly increases operators' mental workload. As a result, these systems struggled to manage dynamic changes at the remote site and lacked sufficient environmental context and an unlimited field of view for operators. This study addresses these limitations by augmenting the virtual replica of the remote environment with a real-time, spatially consistent video stream within the AV-based HMI, enabling operators to better understand dynamic changes at the remote site and enhancing both situational awareness and control precision during teleoperations. Additionally, 3D point clouds and haptic feedback are integrated to create a multi-modal interface that further improves operator perception and interaction with the remote environment. A user study comparing the immersive AV-based HMI with a multi-monocular HMI demonstrated significant improvements in task workload, system usability, spatial presence, and task completion times. Participant feedback further confirmed the system’s ability to improve operator performance.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"167 ","pages":"Article 104260"},"PeriodicalIF":8.2,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125282","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":"Evaluating unsignalized crosswalk safety in the age of autonomous vehicles","authors":"Andrea Avignone ,&nbsp;Marco Bassani ,&nbsp;Beatrice Borgogno ,&nbsp;Brunella Caroleo ,&nbsp;Silvia Chiusano ,&nbsp;Federico Princiotto","doi":"10.1016/j.compind.2025.104259","DOIUrl":"10.1016/j.compind.2025.104259","url":null,"abstract":"<div><div>As autonomous vehicles are poised to enter public roadways, a major concern is their interaction with pedestrians. It requires attention and ability for pedestrians to interact correctly and for autonomous vehicles to detect pedestrians hence avoiding collisions. We propose a complete pipeline to collect, process and elaborate video data to quantitatively assess the possible occurrence of conflicts. It integrates computer vision techniques and a conflict detection system to evaluate these interactions by rigorously implementing the theoretical formulation of two primary metrics: Time-to-Collision (TTC) for the pre-event phase and Post Encroachment Time (PET) for the post-event phase. This study is conducted in a real-world setting with mixed traffic conditions to analyse the differences in pedestrian interactions with both human-operated and autonomous vehicles during daytime. The computation of conflict measures allowed us to identify possible conflicts and assess the safety at an unsignalized crossing, in which pedestrians are exposed to more risky conflicts. The results obtained show a higher incidence of more severe conflicts for interactions between pedestrians and human-operated vehicles, which highlights the caution taken in programming the autonomous vehicle.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"167 ","pages":"Article 104259"},"PeriodicalIF":8.2,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent prediction and soft-sensing of comprehensive production indicators for iron ore sintering: A review","authors":"Sheng Du ,&nbsp;Xian Ma ,&nbsp;Haipeng Fan ,&nbsp;Jie Hu ,&nbsp;Weihua Cao ,&nbsp;Min Wu ,&nbsp;Witold Pedrycz","doi":"10.1016/j.compind.2024.104215","DOIUrl":"10.1016/j.compind.2024.104215","url":null,"abstract":"<div><div>Iron ore sintering is a critical process in iron and steel production, with a substantial impact on overall energy consumption and the emission of various environmental pollutants. Enhancing the efficiency of this process is crucial for achieving sustainability in the iron and steel industry. Accurate prediction and real-time monitoring of comprehensive production indicators are essential for optimizing production and improving energy efficiency. This paper provides a systematic review of intelligent prediction and soft-sensing techniques applied to the iron ore sintering process. It details the mechanisms and operational principles of these technologies, with a focus on key indicators such as quality, thermal state, yield, and energy consumption. This paper explores the current state-of-the-art in four prediction methodologies: mechanism analysis-based methods, data feature analysis-based methods, multi-model fusion-based methods, and operating mode recognition-based methods. Finally, the challenges to the current comprehensive production indicator prediction of the sintering process are pointed out, including the difficulty of dealing with the changing operating mode, the incomplete analysis of image features, and the insufficient consideration of the differences in data distribution. In the future, operating mode recognition approaches, deep learning approaches, transfer learning approaches, and computer vision techniques will have a broad prospect in the comprehensive production indicator prediction of the sintering process.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"165 ","pages":"Article 104215"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804460","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":"Meta-task interpolation-based data augmentation for imbalanced health status recognition of complex equipment","authors":"Jinyuan Li,&nbsp;Wenqing Wan,&nbsp;Yong Feng,&nbsp;Jinglong Chen","doi":"10.1016/j.compind.2024.104226","DOIUrl":"10.1016/j.compind.2024.104226","url":null,"abstract":"<div><div>In the research of health status detection technology for complex equipment such as liquid rocket engines, the extreme working environment hinders the widespread conduct of fault experimental simulations, leading to data scarcity and imbalance. Consequently, the performance of intelligent models deteriorates rapidly with direct training. To address this issue, this paper proposes a meta-task feature space interpolation network model. Firstly, the model uses an encoder to map randomly selected task pairs to a more discriminative feature space, and then interpolates corresponding features and labels within this latent feature space to generate additional tasks, increasing the distribution density of tasks and alleviating the problem of insufficient training tasks. Furthermore, the model leverages self-distillation to improve the learning of label information. By integrating soft labels with supervised labels, it captures the hidden category information of newly interpolated tasks, thereby reducing the impact of class imbalance on model performance. The effectiveness of the proposed method is validated through a series of experiments conducted across three different scenarios. The results demonstrate that the proposed method achieves an average accuracy of 97.91% on the turbopump bearing dataset, which is a significant improvement over the comparative methods.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"165 ","pages":"Article 104226"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884279","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}