Computers in Industry最新文献_第10页

A data-driven approach toward a machine- and system-level performance monitoring digital twin for production lines 采用数据驱动方法，为生产线设计机器和系统级性能监控数字孪生系统

IF 1 1区计算机科学

Computers in Industry Pub Date : 2024-03-26 DOI: 10.1016/j.compind.2024.104086

Yaqing Xu , Yassine Qamsane , Saumuy Puchala , Annette Januszczak , Dawn M. Tilbury , Kira Barton

{"title":"A data-driven approach toward a machine- and system-level performance monitoring digital twin for production lines","authors":"Yaqing Xu , Yassine Qamsane , Saumuy Puchala , Annette Januszczak , Dawn M. Tilbury , Kira Barton","doi":"10.1016/j.compind.2024.104086","DOIUrl":"https://doi.org/10.1016/j.compind.2024.104086","url":null,"abstract":"<div><p>Efficient performance monitoring in production systems holds paramount importance as it enables organizations to optimize their manufacturing processes, enhance productivity, and maintain a competitive edge in the market. Typically, machine and system level performance monitoring systems are investigated independently, whereas an integrated approach that considers both levels can offer valuable insights and benefits. This paper introduces a data-driven approach for evaluating and improving the performance of production lines by monitoring the performance of both individual machines and their interactions as a system. The approach begins with a rigorous methodology for classifying machine states recorded by the Manufacturing Execution System (MES) into finer-grained substates, enabling a comprehensive analysis of machine cycle time variability. Subsequently, these substates are leveraged as a foundation for constructing performance monitoring models at both the machine and system levels, employing probabilistic automata for the machine level and logistic regression for the system level. The system-level performance monitoring model is constructed to predict a Flow metric that enables the prediction of abnormal behaviors and deviations from production targets. This data-driven approach serves as a foundational ingredient of a system-level digital twin, designed to provide production lines with insights that enable proactive implementation of measures aimed at optimizing overall manufacturing efficiency. Through an industrial test case from the automotive industry, the results demonstrate the capability of performance monitoring, capturing errors within confidence intervals, and establishing predictive cause-and-effect relationships between machines within the production system.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"157 ","pages":"Article 104086"},"PeriodicalIF":10.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140295830","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}

引用次数: 0

Construction contract risk identification based on knowledge-augmented language models 基于知识增强语言模型的建筑合同风险识别

IF 1 1区计算机科学

Computers in Industry Pub Date : 2024-03-22 DOI: 10.1016/j.compind.2024.104082

Saika Wong , Chunmo Zheng , Xing Su , Yinqiu Tang

引用次数: 0

Unlocking maintenance insights in industrial text through semantic search 通过语义搜索揭示工业文本中的维护见解

IF 1 1区计算机科学

Computers in Industry Pub Date : 2024-03-21 DOI: 10.1016/j.compind.2024.104083

Syed Meesam Raza Naqvi , Mohammad Ghufran , Christophe Varnier , Jean-Marc Nicod , Kamran Javed , Noureddine Zerhouni

{"title":"Unlocking maintenance insights in industrial text through semantic search","authors":"Syed Meesam Raza Naqvi , Mohammad Ghufran , Christophe Varnier , Jean-Marc Nicod , Kamran Javed , Noureddine Zerhouni","doi":"10.1016/j.compind.2024.104083","DOIUrl":"https://doi.org/10.1016/j.compind.2024.104083","url":null,"abstract":"<div><p>Maintenance records in Computerized Maintenance Management Systems (CMMS) contain valuable human knowledge on maintenance activities. These records primarily consist of noisy and unstructured texts written by maintenance experts. The technical nature of the text, combined with a concise writing style and frequent use of abbreviations, makes it difficult to be processed through classical Natural Language Processing (NLP) pipelines. Due to these complexities, this text must be normalized before feeding to classical machine learning models. Developing these custom normalization pipelines requires manual labor and domain expertise and is a time-consuming process that demands constant updates. This leads to the under-utilization of this valuable source of information to generate insights to help with maintenance decision support. This study proposes a Technical Language Processing (TLP) pipeline for semantic search in industrial text using BERT (Bidirectional Encoder Representations), a transformer-based Large Language Model (LLM). The proposed pipeline can automatically process complex unstructured industrial text and does not require custom preprocessing. To adapt the BERT model for the target domain, three unsupervised domain fine-tuning techniques are compared to identify the best strategy for leveraging available tacit knowledge in industrial text. The proposed approach is validated on two industrial maintenance records from the mining and aviation domains. Semantic search results are analyzed from a quantitative and qualitative perspective. Analysis shows that TSDAE, a state-of-the-art unsupervised domain fine-tuning technique, can efficiently identify intricate patterns in the industrial text regardless of associated complexities. BERT model fine-tuned with TSDAE on industrial text achieved a precision of 0.94 and 0.97 for mining excavators and aviation maintenance records, respectively.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"157 ","pages":"Article 104083"},"PeriodicalIF":10.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140181045","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}

引用次数: 0

Style Adaptation module: Enhancing detector robustness to inter-manufacturer variability in surface defect detection 风格适应模块：在表面缺陷检测中增强检测器对制造商间差异的稳健性

IF 1 1区计算机科学

Computers in Industry Pub Date : 2024-03-20 DOI: 10.1016/j.compind.2024.104084

Chen Li , Xiakai Pan , Peiyuan Zhu , Shidong Zhu , Chengwei Liao , Haoyang Tian , Xiang Qian , Xiu Li , Xiaohao Wang , Xinghui Li

{"title":"Style Adaptation module: Enhancing detector robustness to inter-manufacturer variability in surface defect detection","authors":"Chen Li , Xiakai Pan , Peiyuan Zhu , Shidong Zhu , Chengwei Liao , Haoyang Tian , Xiang Qian , Xiu Li , Xiaohao Wang , Xinghui Li","doi":"10.1016/j.compind.2024.104084","DOIUrl":"https://doi.org/10.1016/j.compind.2024.104084","url":null,"abstract":"<div><p>In recent years, deep learning-based approaches for industrial surface defect detection have shown great promise. To address the domain shift issue among data from different sources in the industrial domain, we present a novel plug-and-play Style Adaptation (SA) module, which endows the equipped defect detector with the capability to exhibit robustness to diverse styles present within the samples. This module effectively leverages datasets sourced from diverse origins while possessing congruent data types. In contrast to other domain adaptation approaches lacking well-defined domain delineations, the SA module generates representations characterized by distinct practical implications and precise mathematical formulations. Moreover, incorporating attention mechanisms reduces the need for manual intervention, allowing the module to focus autonomously on crucial branches in it. Experimental results demonstrate the superior efficacy of our approach compared to state-of-the-art techniques. Furthermore, an authentic dataset from various manufacturers is publicly available for deep learning research and industrial applications. Access the dataset at: <span>https://github.com/THU-PMVAI/MTS3D</span><svg><path></path></svg></p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"157 ","pages":"Article 104084"},"PeriodicalIF":10.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140180064","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}

引用次数: 0

“What’s Going On” with BizDevOps: A qualitative review of BizDevOps practice BizDevOps 的 "进展情况"：对 BizDevOps 实践的定性审查

IF 1 1区计算机科学

Computers in Industry Pub Date : 2024-03-08 DOI: 10.1016/j.compind.2024.104081

Pedro Antunes , Mary Tate

引用次数: 0

Process parameter effects estimation and surface quality prediction for selective laser melting empowered by Bayes optimized soft attention mechanism-enhanced transfer learning 贝叶斯优化软关注机制增强的迁移学习为选择性激光熔化的工艺参数影响估计和表面质量预测赋能

IF 1 1区计算机科学

Computers in Industry Pub Date : 2024-01-09 DOI: 10.1016/j.compind.2023.104066

Jianjian Zhu , Zhongqing Su , Qingqing Wang , Runze Hao , Zifeng Lan , Frankie Siu-fai Chan , Jiaqiang Li , Sidney Wing-fai Wong

{"title":"Process parameter effects estimation and surface quality prediction for selective laser melting empowered by Bayes optimized soft attention mechanism-enhanced transfer learning","authors":"Jianjian Zhu , Zhongqing Su , Qingqing Wang , Runze Hao , Zifeng Lan , Frankie Siu-fai Chan , Jiaqiang Li , Sidney Wing-fai Wong","doi":"10.1016/j.compind.2023.104066","DOIUrl":"https://doi.org/10.1016/j.compind.2023.104066","url":null,"abstract":"<div><p><span><span>Additive Manufacturing (AM), particularly </span>Selective Laser Melting<span> (SLM), has revolutionized the industrial manufacturing sector owing to its remarkable design flexibility and precision. However, it is well known that slight changes in SLM process parameters may highly affect the surface quality of the as-built product. In this paper, we investigate the influence of SLM printing parameters (laser power, </span></span>laser scanning speed<span><span>, layer thickness, and hatch distance) on surface quality and develop a predictive model for surface quality based on the given printing parameters. The developed model is constructed by a Bayesian Optimization and soft Attention mechanism-enhanced Transfer learning (BOAT) framework with superior domain adaptability and generalization capability. Through experimental validation, the effectiveness of the BOAT approach in estimating printing parameters and correlating them with surface quality has been verified. The comprehensive methodology, experimental configurations, prediction results, and ensuing discussions are all presented. This study contributes to providing valuable insights and practical implications for improving the competitiveness and impact of SLM in </span>advanced manufacturing by accurately predicting surface quality with specified printing parameters.</span></p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"156 ","pages":"Article 104066"},"PeriodicalIF":10.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139399239","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}

引用次数: 0

Neural semantic tagging for natural language-based search in building information models: Implications for practice 神经语义标记用于构建信息模型中基于自然语言的搜索：对实践的影响

IF 1 1区计算机科学

Computers in Industry Pub Date : 2023-12-21 DOI: 10.1016/j.compind.2023.104063

Mehrzad Shahinmoghadam , Samira Ebrahimi Kahou , Ali Motamedi

{"title":"Neural semantic tagging for natural language-based search in building information models: Implications for practice","authors":"Mehrzad Shahinmoghadam , Samira Ebrahimi Kahou , Ali Motamedi","doi":"10.1016/j.compind.2023.104063","DOIUrl":"10.1016/j.compind.2023.104063","url":null,"abstract":"<div><p><span><span>While the adoption of open Building Information Modeling (open BIM) standards continues to grow, the inherent complexity and multifaceted nature of the built </span>asset lifecycle<span> data present a critical bottleneck for effective information retrieval. To address this challenge, the research community has started to investigate advanced natural language-based search for building information models. However, the accelerated pace of advancements in deep learning-based natural language processing research has introduced a complex landscape for domain-specific applications, making it challenging to navigate through various design choices that accommodate an effective balance between prediction accuracy and the accompanying computational costs. This study focuses on the semantic tagging of user queries, which is a cardinal task for the identification and classification of references related to building entities and their specific descriptors. To foster adaptability across various applications and disciplines, a </span></span>semantic annotation<span> scheme is introduced that is firmly rooted in the Industry Foundation Classes (IFC) schema. By taking a comparative approach, we conducted a series of experiments to identify the strengths and weaknesses of traditional and emergent deep learning architectures for the task at hand. Our findings underscore the critical importance of domain-specific and context-dependent embedding learning for the effective extraction of building entities and their respective descriptions.</span></p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"155 ","pages":"Article 104063"},"PeriodicalIF":10.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138827023","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}

引用次数: 0

Semi-automated dataset creation for semantic and instance segmentation of industrial point clouds. 为工业点云的语义和实例分割创建半自动数据集。

IF 1 1区计算机科学

Computers in Industry Pub Date : 2023-12-21 DOI: 10.1016/j.compind.2023.104064

August Asheim Birkeland , Marius Udnæs

{"title":"Semi-automated dataset creation for semantic and instance segmentation of industrial point clouds.","authors":"August Asheim Birkeland , Marius Udnæs","doi":"10.1016/j.compind.2023.104064","DOIUrl":"10.1016/j.compind.2023.104064","url":null,"abstract":"<div><p>The current practice for creating as-built geometric Digital Twins (gDTs) of industrial facilities is both labour-intensive and error-prone. In aged industries it typically involves manually crafting a CAD or BIM model from a point cloud collected using terrestrial laser scanners. Recent advances within deep learning (DL) offer the possibility to automate semantic and instance segmentation of point clouds, contributing to a more efficient modelling process. DL networks, however, are data-intensive, requiring large domain-specific datasets. Producing labelled point cloud datasets involves considerable manual labour, and in the industrial domain no open-source instance segmentation dataset exists. We propose a semi-automatic workflow leveraging object descriptions contained in existing gDTs to efficiently create semantic- and instance-labelled point cloud datasets. To prove the efficiency of our workflow, we apply it to two separate areas of a gas processing plant covering a total of <span><math><mrow><mn>40</mn><mspace></mspace><mn>000</mn><mspace></mspace><msup><mrow><mtext>m</mtext></mrow><mrow><mn>2</mn></mrow></msup></mrow></math></span>. We record the effort needed to process one of the areas, labelling a total of 260 million points in 70 h. When benchmarking on a state-of-the-art 3D instance segmentation network, the additional data from the 70-hour effort raises mIoU from 24.4% to 44.4%, AP from 19.7% to 52.5% and RC from 45.9% to 76.7% respectively.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"155 ","pages":"Article 104064"},"PeriodicalIF":10.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0166361523002142/pdfft?md5=866f5e5296cb9cc744004f2c402aba42&pid=1-s2.0-S0166361523002142-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138827014","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}

引用次数: 0

Implementation of a scalable platform for real-time monitoring of machine tools 实施可扩展的机床实时监控平台

IF 1 1区计算机科学

Computers in Industry Pub Date : 2023-12-19 DOI: 10.1016/j.compind.2023.104065

Endika Tapia , Unai Lopez-Novoa , Leonardo Sastoque-Pinilla , Luis Norberto López-de-Lacalle

引用次数: 0

A novel physically interpretable end-to-end network for stress monitoring in laser shock peening 用于激光冲击强化应力监测的新型物理可解释端到端网络

IF 1 1区计算机科学

Computers in Industry Pub Date : 2023-12-15 DOI: 10.1016/j.compind.2023.104060

Rui Qin , Zhifen Zhang , Jing Huang , Zhengyao Du , Xianwen Xiang , Jie Wang , Guangrui Wen , Weifeng He

{"title":"A novel physically interpretable end-to-end network for stress monitoring in laser shock peening","authors":"Rui Qin , Zhifen Zhang , Jing Huang , Zhengyao Du , Xianwen Xiang , Jie Wang , Guangrui Wen , Weifeng He","doi":"10.1016/j.compind.2023.104060","DOIUrl":"https://doi.org/10.1016/j.compind.2023.104060","url":null,"abstract":"<div><p><span><span>The data-driven method based on acoustic emission signals is gradually becoming a hot topic in the field of </span>laser shock peening<span> quality monitoring. Although some existing deep learning methods do provide excellent monitoring accuracy and speed, they lack physical </span></span>interpretability<span><span> in nature, and the opacity of these decisions poses a great challenge to their credibility. The weak interpretability of deep learning models has become the biggest obstacle to the landing of artificial intelligence<span> projects. To overcome this drawback, this paper proposes a monitoring strategy that can achieve physical interpretability in feature extraction, selection and classification, namely, jointly generating monitoring results and explanations. Specifically, it is an end-to-end model that combines convolutional neural units, gated </span></span>recurrent<span> units, and attention mechanisms. Firstly, a wavelet analysis with physical meaning that can be autonomously learned is performed on the acoustic emission. Then, the contribution of features is distinguished based on the correlation of information in different frequency bands, and redundant and noisy features are removed. Finally, the interpretability evaluation of processing quality is realized by using gated recurrent units with attention mechanisms. The effectiveness and reliability of the proposed method are confirmed by the experimental data of both laser shock peening at small and large gradient energies compared to state-of-the-art feature methods, CNN- and LSTM-based models. Most importantly, the physical interpretation of acoustic emission signals during the processing can increase the credibility of decisions and provide a basic logic for on-site judgments by professionals.</span></span></p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"155 ","pages":"Article 104060"},"PeriodicalIF":10.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138657269","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}

引用次数: 0