Advanced Engineering Informatics最新文献

{"title":"Bridge substructure damage morphology identification based on the underwater sonar point cloud data","authors":"Shuaihui Zhang ,&nbsp;Yanjie Zhu ,&nbsp;Wen Xiong ,&nbsp;C.S. Cai ,&nbsp;Jinquan Zhang","doi":"10.1016/j.aei.2024.102936","DOIUrl":"10.1016/j.aei.2024.102936","url":null,"abstract":"<div><div>Bridge underwater foundation inspection is always a prominent and challenging issue due to an unknown and unsafe underwater environment. Effective identification of bridge foundations is significant for the safety assessment of water-related bridges. However, due to the interference of numerous objective factors in the water environment (e.g., water quality, flow velocity, water depth, etc.), reliable and valid data are often difficult to obtain, and the inspection of bridge substructures remains a major challenge, especially for deep water bridge foundations. To solve this problem, a damage morphology identification method based on underwater sonar point cloud data (USPCD) is proposed in this paper for underwater bridge structures. The method is divided into two stages, including potential damage region attention and fine damage morphology identification. The former considers the regional connectivity properties of the damage, focusing on potential damage regions employing a curve fitting method based on iterative median absolute deviation. The latter gives a significant density difference between intact and damaged regions based on the density-based spatial clustering of applications with the noise clustering method to separate damaged data points from normal data points while preserving fine damage morphology features. Based on the swept sonar point cloud of underwater piles from a cross-Yangtze River bridge, we simulated spalling and cavity damage at different scales to comprehensively evaluate our proposed method. The results show that the method can detect damage at different scales and can identify most of the damaged regions. For larger-scale damage, four evaluation indicators are kept at a high level, in which the maximum <em>GTOR</em> and <em>IOU</em> can reach 95.8 % and 85.9 %, respectively. For small-scale damage, based on the synthesized high-resolution point cloud, the method can accurately identify even the damage as small as 12 cm with <em>GTOR</em> above 94 % and <em>IOU</em> over 85 %.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102936"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fault diagnosis of blast furnace based on incomplete multi-source domain adaptation with feature fusion","authors":"Dali Gao,&nbsp;Chunjie Yang,&nbsp;Xiao-Yu Tang,&nbsp;Xiongzhuo Zhu,&nbsp;Xiaoke Huang","doi":"10.1016/j.aei.2024.102946","DOIUrl":"10.1016/j.aei.2024.102946","url":null,"abstract":"<div><div>Aiming at the model mismatch caused by changes in data distribution, transfer learning (TL) has been introduced to fault diagnosis of the blast furnace (BF) ironmaking process. However, most existing TL methods require that the category space of each source and target domain be identical, and ignore the semantic information of multi-source data under domain adaptation. To address these issues, we propose a novel method based on incomplete multi-source domain adaptation with feature fusion for fault diagnosis of BF. Firstly, a multi-scale convolutional network is set to effectively extract diverse features while enabling information interaction through point-wise convolution. Secondly, Transfer Vision Transformer is constructed for each source domain to fuse global and local features, and extract domain-specific knowledge with more semantic information. Finally, the model weights each source classifier based on the inter-domain similarity to obtain the result. Experiments on actual BF data validate the effectiveness of the proposed method.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102946"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D guiding assisted augmented assembly technology with rapid object detection in dynamic environment","authors":"Chengshun Li,&nbsp;Xiaonan Yang,&nbsp;Yaoguang Hu,&nbsp;Shangsi Wu,&nbsp;Jingfei Wang,&nbsp;Peng Wang","doi":"10.1016/j.aei.2024.102857","DOIUrl":"10.1016/j.aei.2024.102857","url":null,"abstract":"<div><div>In the field of industrial assembly, augmented reality (AR) technology has played an important role and demonstrated its enormous development potential in the future. With the current development of product assembly towards customization and diversification, it is difficult to meet the requirements of augmented assembly (AA) by relying on static instructions registered with markers. However, most augmented assembly guidance systems used for dynamic environments are complex, cumbersome, and exhibit high latency, significantly impacting the user experience. In addition, the narrow field of view (Fov) of AR glasses also limits its further application in industrial scene. In response to the above issues, this article proposes an improved 3D guiding assisted augmented assembly technology. Firstly, a lightweight model Yolov7-Slim is proposed to achieve object detection on 2D images, which reduces File size by 26.7 % and improves running speed by 15.3 % compared to the Yolov7-tiny model. Secondly, a 3D positioning algorithm is proposed to achieve the rapid conversion of 2D coordinates to 3D coordinates. Finally, a user-oriented two-stage guidance mechanism is designed to compensate for the limitation of the narrow Fov of AR glasses. To quantify the performance of proposed technology, a 3D guiding assisted augmented assembly system (3DG3AS) was developed and validated in a reducer assembly experiment.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102857"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating TRIZ-inspired guidelines for eco-design using Generative Artificial Intelligence","authors":"C.K.M. Lee ,&nbsp;Jingying Liang ,&nbsp;K.L. Yung ,&nbsp;K.L. Keung","doi":"10.1016/j.aei.2024.102846","DOIUrl":"10.1016/j.aei.2024.102846","url":null,"abstract":"<div><div>Environmental considerations are emerging as stimuli for innovation during the eco-design ideation process. Integrating TRIZ (Teoriya Resheniya Izobretatelskikh Zadatch─Theory of Inventive Problem Solving) methodology into eco-design offers a structured problem-solving approach to address sustainability challenges. However, developing innovative designs requires expertise in TRIZ concepts and access to resources, which makes it a time-consuming process and can limit its application for eco-design innovation quickly. This study leverages the analytical and generative capabilities of large language models (LLMs) to enhance the TRIZ methodology and automate the ideation process in eco-design. An intelligent tool, “Eco-innovate Assistant,” is designed to provide users with eco-innovative solutions with design sketches. Its effectiveness is validated and evaluated through comparative studies. The findings demonstrate the potential of LLMs in automating design processes, catalyzing a transformation in AI-driven innovation and ideation in eco-design.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102846"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An ensembled multilabel classification method for the short-circuit detection of electrolytic refining","authors":"Yusi Dai ,&nbsp;Chunhua Yang ,&nbsp;Hongqiu Zhu ,&nbsp;Can Zhou","doi":"10.1016/j.aei.2024.102919","DOIUrl":"10.1016/j.aei.2024.102919","url":null,"abstract":"<div><div>Short-circuits occurring in the electrolytic refining process of non-ferrous smelting are a main factor that consumes extra energy and affects the metal quality. This paper proposes an ensembled multilabel classification method for short-circuit detection based on infrared images and makes up for the defect of previous methods using object-detection neural networks being hard to directly apply in industrial sites. Different from the object-detection methods, the multilabel classification method does not output the imaging positions but directly obtains the realistic positions, i.e. plate numbers, of the faulty plates. By introducing a new convolutional neural network named FlatNet, no extra work is required to get the realistic positions of the faulty plates. To address the data imbalance inherent to multilabel classification, dynamic weights that pay more attention both to the minority class and difficult samples are presented, forming a bilateral constraint on the missed and the false detections. At the end of the method, we design a greedy ensemble approach driven by validation F1-scores for the promotion of detection performance and stability. Experiments conducted with real-world data verify the effectiveness of the proposed fault detection method.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102919"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on click enhancement strategy of hand-eye dual-channel human-computer interaction system: Trade-off between sensing area and area cursor","authors":"Ya-Feng Niu,&nbsp;Rui Chen,&nbsp;Yi-Yan Wang,&nbsp;Xue-Ying Yao,&nbsp;Yun Feng","doi":"10.1016/j.aei.2024.102880","DOIUrl":"10.1016/j.aei.2024.102880","url":null,"abstract":"<div><div>This study aims to explore the application of click enhancement strategies in a “Sight Line Localization + Hand Triggered” eye-control human–computer interaction system, proposes a click enhancement strategy to solve two critical problems in eye-control human–computer interaction: Midas touch and low spatial accuracy. By conducting ergonomics experiments, we verify that the proposed click enhancement strategy can effectively improve the operational performance of hand-eye dual-channel HCI systems. The experimental results show that the accuracy of the operation can be significantly improved by using a cursor size equal to the diameter of the interaction control and a sensing area size of 1.8 times the diameter of the control. Based on the comprehensive consideration of operation efficiency and comfort, 0.75 times the control diameter of the cursor and 1.8 times the control diameter of the sensing area are the optimal parameter configurations. The results not only solve the problems of Midas touch and low spatial accuracy but also significantly reduce visual fatigue, thus improving the ease of use and robustness of the hand-eye dual-channel human–computer interaction system.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102880"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized machine learning methods for identifying the stiffness loss of CRTS-II slab track based on vehicle vibration signals","authors":"Tao Shi ,&nbsp;Ping Lou ,&nbsp;T.Y. Yang","doi":"10.1016/j.aei.2024.102886","DOIUrl":"10.1016/j.aei.2024.102886","url":null,"abstract":"<div><div>Vehicle loads and environmental actions inevitably cause stiffness loss in CRTS-II slab track. Accurately identifying the stiffness loss of the slab track has been a crucial issue to the operation safety of vehicle-CRTS-II slab track coupled system (VSCS). However, existing identification methods for the slab track conditions which often focus on a single damage condition of track service status are inefficient and laborious. This study proposes optimized machine learning (ML) methods for automatically identifying the stiffness loss of the CRTS-II slab track utilizing vehicle vibration signals. The proposed methods achieve the intelligent identification of fastener and interface damage in the slab track, with high identification efficiency and low manpower cost. Four selected ML methods, i.e., support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), and artificial neural network (ANN) with optimized hyperparameters are developed to identify the stiffness loss of the slab track. 2200 cases from the dynamic model of VSCS under different conditions of fastener and interface failure are generated to train and test the ML models. The proposed ML methods perform well in the training and testing process, demonstrating that the presented ML methods can accurately identify the stiffness loss of the slab track. Furthermore, the stacking-ensemble learning framework is presented to optimize the performance of the above four ML methods for identifying the stiffness loss of the slab track. The maximum improvement in accuracy for the four selected ML models, utilizing the acceleration of vehicle body and bogie, is 109.09 % and 31.58 %, respectively. The stacking generation has strong anti-noise robustness and generalization ability, proving the excellent reliability and stability of the proposed optimized ML methods. The feature importance of the ML method based on the vehicle acceleration is also analyzed. The proposed efficient and capable optimized ML methods are expected to be widely adopted to intelligently identify the complex service status of track structure utilizing vehicle vibration signals.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102886"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Platform service portfolio management (PSPM) of social digitalization platform for cloud-based collaborative product development ecosystem: A structural approach","authors":"Yuguang Bao ,&nbsp;Xianyu Zhang ,&nbsp;Zhihua Chen ,&nbsp;Tongtong Zhou ,&nbsp;Xinguo Ming","doi":"10.1016/j.aei.2024.102854","DOIUrl":"10.1016/j.aei.2024.102854","url":null,"abstract":"<div><div>In the context of digital transformation, social digitalization platform (SDP) emerges and play an important and irreplaceable role in the whole industrial ecosystems. The main value creation purpose of SDP is to develop general technical service portfolios and customized solutions. For SDPs, platform service portfolio management (PSPM) is a really difficult problem. The decision must dynamically respond to the complex external environments of business requirement, technology evolution, and system innovation. The traditional experience-driven decision-making process confuses various factors together, and it is difficult to produce scientific decisions continuously and effectively. To help these platform-kind firms make wise decision, we put forward a novel and practical decision-making methodological framework for PSPM issues. Firstly, a general system architecture model, namely <em>Business-Digitalization-System</em> (BDS) model, is proposed based on system engineering, which helps platform service component identification more refined. Next, the PSPM problem is formalized and modelled as a causal relation graph-based intertwined system analysis procedure. A novel platform service portfolio evaluation method is developed to obtain the priority of the identified service alternatives. The approach can effectively handle the component system importance and its heterogeneous causal interdependencies simultaneously. Meanwhile, the combinatorial manipulation of subjective judgements and objective measure improves the reasonability and efficiency within a multi-stakeholder group. Finally, a case study in the constructive industry is presented and the results of method comparisons show the feasibility and advantages of the methodology.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102854"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interpretable data-driven approach for process flowsheet convergence troubleshooting","authors":"Shifeng Qu,&nbsp;Xinjie Wang,&nbsp;Wenli Du,&nbsp;Feng Qian","doi":"10.1016/j.aei.2024.102873","DOIUrl":"10.1016/j.aei.2024.102873","url":null,"abstract":"<div><div>Practitioners typically alleviate the convergence problem of process flowsheet models through manual adjustment of the convergence-related flowsheet inputs, which is labor-intensive and relies heavily on expert experience. This paper aims to realize fast troubleshooting for process flowsheets with convergence problems and proposes an interpretable approach for the adjustment of the flowsheets to liberate the manpower for process model maintenance. Specifically, the flowsheet convergence problem is addressed from a data-driven perspective for the first time. The correlation between flowsheet inputs selected according to expert knowledge and convergence status is modeled utilizing the tree-based framework to capture the flowsheet convergence behavior. In addition, a novel interpretable adjustment procedure based on an adaptive minimum mean strategy is constructed to automatically identify strongly convergence-related flowsheet inputs and provide them with quantitative adjustment suggestions. The proposed approach shows effectiveness on non-convergence flowsheets with a success rate of up to 92.5%.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102873"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multidomain neural process model based on source attention for industrial robot anomaly detection","authors":"Bo Yang ,&nbsp;Yuhang Huang ,&nbsp;Jian Jiao ,&nbsp;Wenlong Xu ,&nbsp;Lei Liu ,&nbsp;Keqiang Xie ,&nbsp;Nan Dong","doi":"10.1016/j.aei.2024.102910","DOIUrl":"10.1016/j.aei.2024.102910","url":null,"abstract":"<div><div>Industrial robots are vital intelligent equipment in modern industries. Periodic maintenance, which is costly and cannot prevent unexpected failures, is necessary to reduce the probability of failure and extend their service life. Therefore, this study pioneers the application of neural processes in industrial robot anomaly detection. On the basis of the attentive neural process framework, a multidomain fusion neural process (MNP) model based on source attention (SA), which introduces a multidomain path that improves the ability of the model to decouple latent distributions of observed data in industrial environments, is proposed. The multidomain path consists of the following parts: First, a time–frequency domain feature extraction module (TFDFEM) is proposed to extract rich time–frequency domain features from raw signals. Second, a dual-purpose SA module is designed to calibrate the temporal and spectral features within the signal, enabling the model to prioritize relevant features. Last, an SA-based multidomain fusion strategy (MDFS) is developed to fuse and complement features from different domains. Numerous experiments based on robots in an automotive welding and bolt fastening lines show that the MNP achieves an average accuracy of 90.8%, outperforming existing models by at least 6.2%. The average F1 is 94.7%, which outperforms existing models by 4.2%. Therefore, our model provides a promising tool for the state-based maintenance of industrial robots. The code for this project is available at <span><span>https://github.com/hyh7323/Multi-domain-Neural-Process</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102910"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}