Computers in Industry最新文献_第3页

Streamlining Assembly Instruction Design (S-AID): A comprehensive systematic framework 精简装配指令设计（S-AID）：一个全面的系统框架

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-02-01 DOI: 10.1016/j.compind.2024.104232

Mirco Bartolomei , Federico Barravecchia , Luca Mastrogiacomo , Davide Maria Gatta , Fiorenzo Franceschini

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 S-AID, 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 S-AID 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.

装配说明是用于指导各个制造部门的产品装配的详细指令。随着生产过程变得更加灵活，装配说明在满足严格的效率和质量标准方面的重要性变得越来越明显。然而，装配说明的发展往往仍然是非结构化的，主要依赖于设计师的经验或个人技能。本文旨在通过追求三个主要目标来解决这些问题：(i)破译汇编过程和表征它的信息，从而提供指令成分的分类；（ii）提出一个框架，以评估可传播此类信息的各种格式；（iii）引入一种循序渐进的方法，名为S-AID，它为设计人员在指令设计阶段提供了一致的方法。总体而言，本研究提供了汇编指令构建块的严格分类，并定义了它们与各种指令格式的关系。此外，通过提出一种系统的设计方法，本工作旨在解决传统教学设计过程中常见的冗余和不一致问题。所提出的方法是用一个现实世界的案例研究，涉及机械设备的组装说明。最后，通过与其他指令集的比较分析，对S-AID方法的有效性进行了定量评估，重点是过程失败、装配完成时间和感知认知负荷等指标。

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引用次数: 0

Non-contact rPPG-based human status assessment via a spatial–temporal attention feature fusion network with anti-aliasing 基于特征融合嵌入抗混叠的非接触rppg工业人体状态评估

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-02-01 DOI: 10.1016/j.compind.2024.104227

Qiwei Xue , Xi Zhang , Yuchong Zhang , Amin Hekmatmanesh , Huapeng Wu , Yuntao Song , Yong Cheng

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.

远程光电容积脉搏波描记（rPPG）是一种具有成本效益的非接触式技术，可以通过提取心率（HR）等重要信息来实时监测生理状态。这种能力可以评估疲劳和压力，帮助通过早期识别危险情况来防止事故的发生。使用rPPG进行持续监测可降低操作风险，有助于提高工业和医疗环境的安全性。然而，rPPG的性能受到工业环境中复杂背景和面部运动的挑战，这使得特征提取变得复杂。为了解决这些问题，本文提出了一种用于人类状态评估的具有抗混叠的时空注意力特征融合网络（ST-ASENet）。ST-ASENet对来自多个感兴趣区域（ROI）的时空面部信号进行编码，并通过注意机制增强特征提取。该网络在下采样过程中通过低通滤波实现抗混叠，提高了rPPG信号在复杂环境下的精度。它计算HR、呼吸率（RR）和心率变异性（HRV）来评估状态。此外，引入了机器人操作员因素评估（ROFA）数据集，具有不同的个体和环境，以提高ST-ASENet的鲁棒性。实验结果表明，ST-ASENet在人力资源估计方面优于最先进的方法，并在各种工业场景中显示出有效性。拟议的方法提高了操作效率和数据驱动的方法，以实现以人为本的安全，使rPPG在以健康为重点的现代工作场所中变得非常宝贵。

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引用次数: 0

Deep hierarchical sorting networks for fault diagnosis of aero-engines 航空发动机故障诊断的深度层次分类网络

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-02-01 DOI: 10.1016/j.compind.2024.104229

Jinlei Wu, Lin Lin, Dan Liu, Song Fu, Shiwei Suo, Sihao Zhang

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 %.

在现代工业中，对航空发动机进行及时的健康评估对于确保其正常功能和航空运行安全至关重要。然而，在航空发动机的运行数据采集过程中，由于某些传感器的传输延迟，影响故障特征可能会出现滞后甚至被淹没。此外，传统的深度神经网络难以提取间隔点数据中的这些特征。此外，在航空发动机故障诊断中，正常样本的数量明显高于故障样本的数量。因此，传统的深度神经网络往往只关注正常样本，而忽略了故障样本，增加了漏诊或误诊的风险。针对这些问题，本文提出了一种基于状态点分层排序的并行卷积神经网络（FSHSM-PCNN），通过分层排序模块提高不同层次状态点数据之间的协同效果，并通过并行卷积神经网络高效提取故障信息。首先，通过基于故障显著性的分层排序模块（FSHSM）对原始样本中的状态点数据沿时间维度进行内部排序，排序后得到的不同层次的状态点数据显示出增强的协同效应。其次，利用并行卷积神经网络提取时间状态特征和增强协同特征，将融合后的信息用于故障诊断；最后，利用航空发动机的实际监测数据对所提出的FSHSM-PCNN进行了性能评估。实验结果表明，该方法能够有效地从监测数据中提取故障特征。与烧蚀研究中的其他方法相比，所提出的方法在航空发动机故障诊断中的平均性能分别提高了12.46 %、7.07 %和12.62 %。在不平衡数据集的诊断任务中，其准确率超过其他方法至少5.01 %。

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引用次数: 0

An integrated approach for enhanced early-phase space system design and optimization

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-01-31 DOI: 10.1016/j.compind.2025.104258

Yutong Zhang , Dong Ye , Cheng Wei , Zhaowei Sun

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.

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引用次数: 0

A triple semantic-aware knowledge distillation network for industrial defect detection

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-01-30 DOI: 10.1016/j.compind.2025.104252

Zhitao Wen, Jinhai Liu, He Zhao, Qiannan Wang

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).

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引用次数: 0

Collaborative fault tolerance for cyber–physical systems: The detection stage

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-01-30 DOI: 10.1016/j.compind.2025.104253

Luis Piardi , André Schneider de Oliveira , Pedro Costa , Paulo Leitão

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.

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引用次数: 0

Automated construction contract analysis for risk and responsibility assessment using natural language processing and machine learning

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-01-25 DOI: 10.1016/j.compind.2025.104251

Irem Dikmen , Gorkem Eken , Huseyin Erol , M. Talat Birgonul

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.

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引用次数: 0

Domain ontology to integrate building-integrated photovoltaic, battery energy storage, and building energy flexibility information for explicable operation and maintenance

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-01-23 DOI: 10.1016/j.compind.2025.104250

Xiaoyue Yi , Llewellyn Tang , Reynold Cheng , Mengtian Yin , Yu Zheng

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&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&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&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&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&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&M, highlighting the potential of ontology-based approaches in BIPV, BES, and BEF data integration and knowledge inference.

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引用次数: 0

Acoustic signal-based wear monitoring for belt grinding tools with pyramid-structured abrasives using BO-KELM 基于声信号的金字塔结构磨料带磨具磨损监测

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-01-03 DOI: 10.1016/j.compind.2024.104235

Yingjie Liu , Wenxi Wang , Xiaoyu Zhao , Shudong Zhao , Lai Zou , Chao Wang

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.

金字塔形磨粒带近年来在复杂曲面的精密加工领域得到了广泛的应用。然而，连续磨损直接影响其加工性能和质量。缺乏有效的工程监测方法限制了这种砂带的进一步应用。针对这一问题，本研究提出了一种基于BO-KELM模型的金字塔结构磨粒带磨损状态声信号监测方法。与传统方法相比，该方法可以自动调整模型超参数，节省了人工调整时间，提高了模型性能。提出了一种准确量化磨粒带磨损状态的Rat指数。当磨损状态数设置为10时，基于精度矩阵的准确率为97.88 %，精密度为95.90 %，召回率为96.01 %，F1分数为0.9592。当磨损状态的数量减少时，该模型的性能更好。

引用次数: 0

Predictive analysis-based sustainable waste management in smart cities using IoT edge computing and blockchain technology 利用物联网边缘计算和区块链技术在智慧城市中进行基于预测分析的可持续废物管理

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-01-03 DOI: 10.1016/j.compind.2024.104234

C. Anna Palagan , S. Sebastin Antony Joe , S.J. Jereesha Mary , E. Edwin Jijo

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.

随着人口的增长，有效的废物管理已成为发展智慧城市的关键挑战。传统的废物管理系统往往效率低下，导致不必要的行程、高昂的运营成本、难以追踪废物以及资源的低效利用。提出的工作旨在利用区块链联合学习集成基于实时预测分析的废物收集和处理过程，克服指定的挑战。最初，物联网传感器安装在不同地点的垃圾箱中，以监测废物堆积的深度。本地边缘网关对收集到的数据进行预处理，随机森林模型对数据进行分析以确定bin状态。汇总的数据被发送到一个全球模型，该模型可以预测废物产生的总体趋势。此外，处理后的数据被安全地记录在区块链网络上，并结合智能合约，通过一个名为D-App的分散应用程序进行访问，该应用程序为安排废物收集提供实时更新，与用户和利益相关者进行有效沟通，以访问实时数据以监控垃圾箱状态，并跟踪废物收集卡车。结果，该模型使用RF算法预测bin状态的准确率为99.25 %，区块链帮助实现了95% %的用户信任水平。因此，建议的工作减少了运营费用，优化了废物收集路线，做出了更好的决策，并为可持续的废物管理提供了可扩展的解决方案。

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引用次数: 0