Computers in Industry最新文献

{"title":"Streamlining Assembly Instruction Design (S-AID): A comprehensive systematic framework","authors":"Mirco Bartolomei ,&nbsp;Federico Barravecchia ,&nbsp;Luca Mastrogiacomo ,&nbsp;Davide Maria Gatta ,&nbsp;Fiorenzo Franceschini","doi":"10.1016/j.compind.2024.104232","DOIUrl":"10.1016/j.compind.2024.104232","url":null,"abstract":"<div><div>Assembly instructions are detailed directives used to guide the assembly of products across various manufacturing sectors. As production processes evolve to become more flexible, the significance of assembly instructions in meeting rigorous efficiency and quality standards becomes increasingly pronounced. Nevertheless, the development of assembly instructions often remains unstructured and predominantly dependent on the experience or personal skills of the designer. This paper aims to address these issues by pursuing three main goals: (i) deciphering the assembly process and the information that characterizes it, thereby providing a taxonomy of instruction constituents; (ii) presenting a framework to assess the various formats in which such information can be communicated; and (iii) introducing a step-by-step method, named <em>S-AID</em>, which offers a consistent methodology for designers during the instruction design phase. Overall, this research provides a rigorous taxonomy of the building blocks of assembly instructions and defines their relationships with various instruction formats. Furthermore, by proposing a systematic design method, this works aims to address the redundancy and inconsistency commonly encountered in traditional instruction design processes. The proposed methodology is illustrated using a real-world case study involving the assembly of a mechanical equipment. Finally, the effectiveness of the <em>S-AID</em> method was evaluated quantitatively through comparative analysis with other instruction sets, focusing on metrics such as process failures, assembly completion time, and perceived cognitive load.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"165 ","pages":"Article 104232"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884277","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":"Non-contact rPPG-based human status assessment via a spatial–temporal attention feature fusion network with anti-aliasing","authors":"Qiwei Xue ,&nbsp;Xi Zhang ,&nbsp;Yuchong Zhang ,&nbsp;Amin Hekmatmanesh ,&nbsp;Huapeng Wu ,&nbsp;Yuntao Song ,&nbsp;Yong Cheng","doi":"10.1016/j.compind.2024.104227","DOIUrl":"10.1016/j.compind.2024.104227","url":null,"abstract":"<div><div>Remote Photoplethysmography (rPPG) is a cost-effective and non-contact technology that enables real-time monitoring of physiological status by extracting vital information such as heart rate (HR). This capability enables the assessment of fatigue and stress, helping to prevent accidents by identifying risky conditions early. Continuous monitoring with rPPG reduces operational risks, contributing to safer industrial and medical environments. However, the performance of rPPG is challenged by complex backgrounds and facial motions in industrial environments, which complicates feature extraction. To address these challenges, this paper proposes a spatial–temporal attention feature fusion network with anti-aliasing (ST-ASENet) for human status assessment. The ST-ASENet encodes spatial–temporal facial signals from multiple regions of interest (ROI) and enhances feature extraction through the attention mechanism. The network integrates anti-aliasing by low-pass filtering during the downsampling process to improve the accuracy of rPPG signals in complex environments. It calculates HR, respiratory rate (RR), and heart rate variability (HRV) for status evaluation. Additionally, the Robotics Operator Factors Assessment (ROFA) dataset is introduced, featuring diverse individuals and environments to improve the robustness of ST-ASENet. Experimental results demonstrate that ST-ASENet outperforms state-of-the-art methods in HR estimation and shows effectiveness across various industrial scenarios. The proposed method fosters operational efficiency and a data-driven approach to human-centric safety, making rPPG invaluable in modern, health-focused workplaces.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"165 ","pages":"Article 104227"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884278","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":"Deep hierarchical sorting networks for fault diagnosis of aero-engines","authors":"Jinlei Wu,&nbsp;Lin Lin,&nbsp;Dan Liu,&nbsp;Song Fu,&nbsp;Shiwei Suo,&nbsp;Sihao Zhang","doi":"10.1016/j.compind.2024.104229","DOIUrl":"10.1016/j.compind.2024.104229","url":null,"abstract":"<div><div>In modern industry, timely health assessments of aero-engines are crucial for ensuring their proper functionality and the safety of aviation operations. However, during the collection of operating data for aero-engines, influential fault features may exhibit hysteresis or even overwhelmed due to transmission delays in some sensors. Furthermore, these features in the data at interval points are difficult to extract using traditional deep neural networks. Moreover, in aero-engine fault diagnosis, the number of normal samples is significantly higher than that of fault samples. As a result, traditional deep neural networks tend to focus on normal samples while fault samples are neglected, increasing the risk of missed diagnoses or misdiagnoses. To address these problems, this paper proposes a parallel convolutional neural network based on hierarchical sorting of state points (FSHSM-PCNN), to improve the synergistic effect between state point data at different hierarchical levels via the hierarchical sorting module, and to efficiently extract fault information via the parallel convolutional neural network. First, the state point data in the original samples is internally sorted along the time dimension by the fault significance-based hierarchical sorting module (FSHSM), and the different levels of state point data obtained after sorting reveal a reinforced synergistic effect. Second, a parallel convolutional neural network is developed to extract temporal status features and reinforced synergistic features, and the fused information is used for fault diagnosis. Finally, the performance of the proposed FSHSM-PCNN is evaluated using actual monitoring data from aero-engines. The experimental results show that the proposed method is effective in extracting fault features from the monitoring data. Compared to other methods in the ablation study, the proposed method improves average performance in aero-engine fault diagnosis by 12.46 %, 7.07 %, and 12.62 %, respectively. In diagnosis tasks with imbalanced datasets, its accuracy exceeds that of other methods by at least 5.01 %.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"165 ","pages":"Article 104229"},"PeriodicalIF":8.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884280","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 integrated approach for enhanced early-phase space system design and optimization","authors":"Yutong Zhang ,&nbsp;Dong Ye ,&nbsp;Cheng Wei ,&nbsp;Zhaowei Sun","doi":"10.1016/j.compind.2025.104258","DOIUrl":"10.1016/j.compind.2025.104258","url":null,"abstract":"<div><div>The integration of Model-Based Systems Engineering (MBSE) and Multidisciplinary Design Analysis and Optimization (MDAO) presents a powerful opportunity to enhance early-stage system design, particularly for complex space systems. However, the lack of efficient integration between these methods results in limitations such as unclear boundary between domain models, reduced automation, and challenges in maintaining traceability of optimization results. Overcoming these barriers is essential for conducting high-quality trade studies in systems engineering. In this work, we propose a novel framework that integrates MDAO with MBSE to streamline system modeling, optimization, and verification. This approach enables the seamless exchange of knowledge between design and optimization models, while performing optimizations and managing results directly within the MBSE environment. By using MBSE as a central knowledge repository, the framework minimizes errors and improves the traceability of optimization processes. Case studies demonstrate that this framework enhances both efficiency and accuracy during the early design phases of space mission development. Our findings indicate that integrating MDAO with MBSE allows for comprehensive system evaluation and more informed decision-making, ultimately improving the quality and efficiency of the design process. This integrated framework offers a flexible, scalable solution for multidisciplinary optimization, making it a valuable tool for the design of future complex systems.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"166 ","pages":"Article 104258"},"PeriodicalIF":8.2,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125025","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 triple semantic-aware knowledge distillation network for industrial defect detection","authors":"Zhitao Wen,&nbsp;Jinhai Liu,&nbsp;He Zhao,&nbsp;Qiannan Wang","doi":"10.1016/j.compind.2025.104252","DOIUrl":"10.1016/j.compind.2025.104252","url":null,"abstract":"<div><div>Knowledge distillation (KD) is a powerful model compression technique that aims to transfer knowledge from heavy teacher networks to compact student networks via distillation. However, effectively transferring semantic knowledge in industrial settings poses significant challenges. On one hand, the appearance of defects (e.g., size and shape) may vary considerably due to the influence of the industrial site, which potentially weakens the semantic associations between class-specific features. On the other hand, agnostic background interference (e.g., spike anomalies and low light) may foster semantic ambiguity of class-specific features. As such, the weakened semantic associations and fostered semantic ambiguities hinder the efficacy and adequacy of knowledge transfer in KD. To mitigate these limitations, we propose a triple semantic-aware knowledge distillation (TSKD) network for industrial defect detection. TSKD contains three refinements, i.e., dual-relation distillation (DRD), decoupled expert distillation (DED), and cross-response distillation (CRD). Specifically, DRD employs graph reasoning networks to strengthen semantic associations at both the instance and pixel levels, DED enhances semantic explicitness by decoupling foreground and background features while injecting expert priors, and CRD further captures task-specific semantic response knowledge. By integrating these components, TSKD can effectively perceive triple semantic knowledge of relations, features, and responses, ensuring more robust and comprehensive knowledge transfer. Experimental evaluations on two challenging industrial datasets show that TSKD can significantly improve detector performance (MFL-DET: 98.9% mAP; NEU-DET: 81.0% mAP) and compress computation (MFL-DET: 19.7M Params and 105 FPS; NEU-DET: 19.7M Params and 116 FPS).</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"166 ","pages":"Article 104252"},"PeriodicalIF":8.2,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125033","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":"Collaborative fault tolerance for cyber–physical systems: The detection stage","authors":"Luis Piardi ,&nbsp;André Schneider de Oliveira ,&nbsp;Pedro Costa ,&nbsp;Paulo Leitão","doi":"10.1016/j.compind.2025.104253","DOIUrl":"10.1016/j.compind.2025.104253","url":null,"abstract":"<div><div>In the era of Industry 4.0, fault tolerance is essential for maintaining the robustness and resilience of industrial systems facing unforeseen or undesirable disturbances. Current methodologies for fault tolerance stages namely, detection, diagnosis, and recovery, do not correspond with the accelerated technological evolution pace over the past two decades. Driven by the advent of digital technologies such as Internet of Things, cloud and edge computing, and artificial intelligence, associated with enhanced computational processing and communication capabilities, local or monolithic centralized fault tolerance methodologies are out of sync with contemporary and future systems. Consequently, these methodologies are limited in achieving the maximum benefits enabled by the integration of these technologies, such as accuracy and performance improvements. Accordingly, in this paper, a collaborative fault tolerance methodology for cyber–physical systems, named Collaborative Fault * (CF*), is proposed. The proposed methodology takes advantage of the inherent data analysis and communication capabilities of cyber–physical components. The proposed methodology is based on multi-agent system principles, where key components are self-fault tolerant, and adopts collaborative and distributed intelligence behavior when necessary to improve its fault tolerance capabilities. Experiments were conducted focusing on the fault detection stage for temperature and humidity sensors in warehouse racks. The experimental results confirmed the accuracy and performance improvements under CF* compared with the local methodology and competitiveness when compared with a centralized approach.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"166 ","pages":"Article 104253"},"PeriodicalIF":8.2,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125026","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":"Automated construction contract analysis for risk and responsibility assessment using natural language processing and machine learning","authors":"Irem Dikmen ,&nbsp;Gorkem Eken ,&nbsp;Huseyin Erol ,&nbsp;M. Talat Birgonul","doi":"10.1016/j.compind.2025.104251","DOIUrl":"10.1016/j.compind.2025.104251","url":null,"abstract":"<div><div>Construction contracts contain critical risk-related information that requires in-depth examination, yet tight schedules for bidding limit the possibility of comprehensive review of extensive documents manually. This research aims to develop models for automating the review of construction contracts to extract information on risk and responsibility that will provide inputs for risk management plans. Models were trained on 2268 sentences from International Federation of Consulting Engineers templates and tested on an actual construction project contract containing 1217 sentences. A taxonomy classified sentences into Heading, Definition, Obligation, Risk, and Right categories with related parties of Contractor, Employer, and Shared. Twelve models employing diverse Natural Language Processing vectorization techniques and Machine Learning algorithms were implemented and benchmarked based on accuracy and F1 score. Binary classification of sentence types and an ensemble method integrating top models were further applied to improve performance. The best model achieved 89 % accuracy for sentence types and 83 % for related parties, demonstrating the capabilities of automated contract review for identification of risk and responsibilities. Adopting the proposed approach can significantly expedite contract reviews to support risk management activities, bid preparation processes and prevent disputes caused by overlooking risks and responsibilities.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"166 ","pages":"Article 104251"},"PeriodicalIF":8.2,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143055234","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":"Domain ontology to integrate building-integrated photovoltaic, battery energy storage, and building energy flexibility information for explicable operation and maintenance","authors":"Xiaoyue Yi ,&nbsp;Llewellyn Tang ,&nbsp;Reynold Cheng ,&nbsp;Mengtian Yin ,&nbsp;Yu Zheng","doi":"10.1016/j.compind.2025.104250","DOIUrl":"10.1016/j.compind.2025.104250","url":null,"abstract":"<div><div>Building-integrated photovoltaics (BIPV) incorporated with battery energy storage (BES) and building energy flexibility (BEF) system is nowadays increasingly prevalent. During the operation and maintenance (O&amp;M) of BIPV, BES, and BEF, various knowledge is contained and generated. This highlights information interaction among systems and the demand for incorporating diverse domain knowledge. However, these systems remain relatively isolated during O&amp;M and suffer from inadequate machine-readable knowledge representation. In the era of semantic web technology, ontology-based methods are promising to integrate heterogeneous information. This study developed a domain ontology named “BIPV-BES-BEF” to integrate BIPV, BES, and BEF O&amp;M information by enriching ontology semantics through relevant standards and leveraging existing ontology resources. In the process ontology construction, classes associated with BIPV, BES, and BEF were initially identified from relevant ontologies based on concepts in authorized codes. The classes with high cosine similarity within these recognized classes were subsequently integrated. Concepts and rules concerning the O&amp;M of BIPV, BES, and BEF from relevant standards were then incorporated to the ontology and semantic web rules. The resulting ontology consists of a total of 2595 axioms and 649 classes, encompassing comprehensive concepts related to BIPV, BES, and BEF components, system specifics, assessment criteria, as well as O&amp;M elements. The built ontology was assessed to be coherent and capable of reasoning through the built knowledge. This study contributes to an ontology purposing BIPV, BES, and BEF O&amp;M, highlighting the potential of ontology-based approaches in BIPV, BES, and BEF data integration and knowledge inference.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"166 ","pages":"Article 104250"},"PeriodicalIF":8.2,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143055236","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":"Acoustic signal-based wear monitoring for belt grinding tools with pyramid-structured abrasives using BO-KELM","authors":"Yingjie Liu ,&nbsp;Wenxi Wang ,&nbsp;Xiaoyu Zhao ,&nbsp;Shudong Zhao ,&nbsp;Lai Zou ,&nbsp;Chao Wang","doi":"10.1016/j.compind.2024.104235","DOIUrl":"10.1016/j.compind.2024.104235","url":null,"abstract":"<div><div>Pyramid-structured abrasive belts have been widely used in the field of precision machining of complex surfaces over recent years. However, continuous wear directly affects their machining performance and quality. The lack of effective engineering monitoring methods limits the further application of such abrasive belts. To address this issue, this study presents an acoustic signal monitoring method for the wear state of pyramid-structured abrasive belts based on the BO-KELM model. Compared with traditional methods, the proposed method can automatically adjust model hyperparameters, saving manual tuning time and improving model performance. A Rat index is proposed, which accurately quantifies the wear state of the abrasive belt. When the number of wear states is set to 10, the proposed method achieves precision matrix-based accuracy, precision, recall, and F1 score values of 97.88 %, 95.90 %, 96.01 %, and 0.9592, respectively. The model performs even better when the number of wear states is reduced.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"166 ","pages":"Article 104235"},"PeriodicalIF":8.2,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936016","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":"Predictive analysis-based sustainable waste management in smart cities using IoT edge computing and blockchain technology","authors":"C. Anna Palagan ,&nbsp;S. Sebastin Antony Joe ,&nbsp;S.J. Jereesha Mary ,&nbsp;E. Edwin Jijo","doi":"10.1016/j.compind.2024.104234","DOIUrl":"10.1016/j.compind.2024.104234","url":null,"abstract":"<div><div>Effective waste management has become the key challenge in developing smart cities with the increase in population. Traditional waste management systems are often inefficient, which leads to unnecessary trips, high operational costs, difficulties in tracking waste, and the inefficient use of resources. The proposed work aims to integrate real-time predictive analysis-based waste collection and disposal processes using federated learning with blockchain, overcoming the challenges specified. Initially, IoT sensors were installed in waste bins across different sites to monitor the depth of waste accumulated. Local edge gateways preprocess the collected data, which the random forest model analyzes to determine the bin status. The aggregated data is sent to a global model that predicts overall waste generation trends. Furthermore, the processed data is securely recorded on a blockchain network combined with smart contracts, accessed through a decentralized application called D-App, which gives real-time updates for scheduling waste collection, performs efficient communication with users and stakeholders to access real-time data to monitor bin status, and track waste collection trucks. As a result, the model predicts bin status with 99.25 % accuracy using an RF algorithm and blockchain helped achieve a user trust level by 95 %. Thus, the proposed work reduces operational expenses, optimizes waste collection routes, makes better decisions, and provides a scalable solution for sustainable waste management.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"166 ","pages":"Article 104234"},"PeriodicalIF":8.2,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936017","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}