Advanced Engineering Informatics最新文献

{"title":"Digital twin-based smart shop-floor management and control: A review","authors":"Cunbo Zhuang ,&nbsp;Lei Zhang ,&nbsp;Shimin Liu ,&nbsp;Jiewu Leng ,&nbsp;Jianhua Liu ,&nbsp;Fengque Pei","doi":"10.1016/j.aei.2024.103102","DOIUrl":"10.1016/j.aei.2024.103102","url":null,"abstract":"<div><div>Propelled by the latest advancements in information technology, shop-floor management and control (SMC) is transitioning towards a more intelligent paradigm, predominantly marked by data-driven insights and the integration of virtual reality. The digital twin (DT) stands out as a pivotal technology for the realization of cyber-physical systems, and its role in smart shop-floor management and control (SSMC) has attracted significant interest from both the industrial sector and academic circles. However, the application of DT in achieving SSMC remains diverse and lacks a structured methodology. In light of this, this review provides an in-depth analysis and discussion of the current state, limitations, and prospective trends of DT in SSMC. Initially, a DT-based SSMC framework is introduced to guide the subsequent literature review and thematic discussions. This is followed by an examination of DT-based SSMC research across four key dimensions: the development of shop-floor DT models, dynamic monitoring and forecasting of the shop-floor leveraging DT, DT-assisted shop-floor scheduling, and DT-driven production process control. The review culminates with an outline of challenges and future research directions for DT-based SSMC. This comprehensive review not only enhances researchers’ comprehension of SSMC but also offers a valuable reference for the continued application and integration of DT within this domain.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103102"},"PeriodicalIF":8.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136972","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":"Advancements in AI-Driven detection and localisation of solar panel defects","authors":"Ali Ghahremani,&nbsp;Scott D. Adams,&nbsp;Michael Norton,&nbsp;Sui Yang Khoo,&nbsp;Abbas Z. Kouzani","doi":"10.1016/j.aei.2024.103104","DOIUrl":"10.1016/j.aei.2024.103104","url":null,"abstract":"<div><div>Renewable energy production has experienced rapid growth over the past three decades and is projected to triple its global capacity by 2030. Given that the utilisation of solar photovoltaic (PV) technology plays a vital role in generating renewable electricity, it is crucial to continuously monitor the condition of solar panels because a variety of defects can significantly reduce their power production. In this paper, we review the latest artificial intelligence (AI) algorithms developed for inspecting solar panels. We also discuss various low-resource hardware systems used to execute these algorithms. AI algorithms are trained using datasets and images, including optical, infrared, and electroluminescence images of solar panels. These images can be captured by unmanned aerial vehicles (UAVs), ground vehicles, and fixed cameras. In this paper, we compare the precision, accuracy, and recall rates of a selection of reviewed AI algorithms. To gain a deeper understanding of these AI algorithms, we introduce a generic framework of AI-driven systems that can autonomously detect and localise solar panel defects and we analyse the literature based on this framework. Some of the main AI and image processing algorithms reviewed are YOLO V5 BDL, weight imprinting, custom-designed CNN, modified edge detection, fuzzy-based edge detection, and the modified Canny algorithm. We also discuss the main hardware systems used to execute image processing algorithms to localise and detect defects in solar panels: the central processing unit (CPU), field programmable gate array (FPGA), and graphics processing unit (GPU). Finally, as a future direction, we suggest developing image processing algorithms specifically designed for hardware systems tailored for machine learning, such as tensor processing units (TPUs). This development would further enhance the capabilities of solar panel inspection and defect detection.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103104"},"PeriodicalIF":8.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130278","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":"Identification and precise optimization of key assembly error links for complex aviation components driven by mechanism and data fusion model","authors":"Feiyan Guo ,&nbsp;Zhang Yongliang ,&nbsp;Song Changjie ,&nbsp;Sha Xiliang","doi":"10.1016/j.aei.2024.103059","DOIUrl":"10.1016/j.aei.2024.103059","url":null,"abstract":"<div><div>As assembling complex aviation products, due to factors such as part deformation under loads, numerous process parameters, and complex error transmission path, the effective identification and optimization of key error links that affecting assembly accuracy significantly is challenging. In this paper, a mechanism and data fusion method for solving this problem was proposed. Firstly, the geometric-physical coupling relationship among composite thin-walled parts and the entire locating/clamping/joining/rebounding operations was analyzed. Then with the actual error information, the Jacobian-torsor matrix that representing error accumulation relationship was modified, and assembly error was calculated with the mechanism model. Secondly, with actual data processing solution to obtain the deviation of theoretical calculation results, the fusion model of integrating mechanism and data analysis results was proposed for predicting the final assembly accuracy. Subsequently, with massive data samples from the fusion model, the Sobol method was adopted to gain the global sensitivity coefficients of different error elements, and the key error links could be identified. Thirdly, with the accurate error fusion results, three single tolerance optimization models for the entire production process were established, i.e. manufacturing cost, assembly quality loss and repair cost. Then a weight parameters design method was proposed, which can avoid the conflict phenomena of data imbalance and optimization deviation problems among different goals, and the multi-objective tolerance allocation model was solved with intelligent algorithm. Finally, for the assembly work of wing-box component, key error links that having an obvious impact on the profile gap and step difference accuracy were identified and optimized, and beneficial quality/efficiency results were gained. This research could provide a strong interpretability for assembly accuracy analysis results, and a good applicability to practical assembly site.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103059"},"PeriodicalIF":8.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129625","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":"Robust operating performance assessment of flotation processes using convolutional neural networks and feature learning","authors":"Runda Jia ,&nbsp;Mingxuan Ren ,&nbsp;Jinglong Wang ,&nbsp;Feng Yu ,&nbsp;Dakuo He","doi":"10.1016/j.aei.2024.103087","DOIUrl":"10.1016/j.aei.2024.103087","url":null,"abstract":"<div><div>The use of computer vision, rather than manual observation, to assess flotation performance based on froth characteristics is crucial for optimizing and controlling the flotation process. Convolutional neural networks (CNNs) are widely employed for image recognition tasks related to evaluating flotation operating performance. However, previous studies have often overlooked the quality of feature learning within these networks, resulting in limited robustness, especially when industrial applications encounter image distortions that challenge network performance.</div><div>To address this issue, this paper proposes a CNN-based algorithm for robust assessment of flotation operating performance, focusing on learning features that accurately reflect froth characteristics. The network is guided through regression training to prioritize froth-specific features, while classification training enhances its ability to evaluate flotation performance. Iterative optimization is achieved by adjusting the regression training loss using feedback from classification results and expert knowledge, thereby refining the network’s performance.</div><div>Experimental results from industrial applications validate the effectiveness of the proposed algorithm, demonstrating its ability to learn key features of froth images and showing high robustness under various types and levels of image distortion.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103087"},"PeriodicalIF":8.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129698","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":"LGGFormer: A dual-branch local-guided global self-attention network for surface defect segmentation","authors":"Gaowei Zhang ,&nbsp;Yang Lu ,&nbsp;Xiaoheng Jiang ,&nbsp;Shaohui Jin ,&nbsp;Shupan Li ,&nbsp;Mingliang Xu","doi":"10.1016/j.aei.2024.103099","DOIUrl":"10.1016/j.aei.2024.103099","url":null,"abstract":"<div><div>In industrial manufacturing, efficient and accurate surface defect detection is paramount. Recently, CNN-based defect segmentation networks have achieved significant success but have limitations in capturing global contextual information. Although Transformer models excel in global modeling, they often lack sufficient attention to local details. To combine the advantages of CNN and Transformer, this paper proposes a dual-branch local-guided global self-attention network (LGGFormer) for Surface Defect Segmentation. Considering the unique characteristics and computational differences between CNN and Transformer, we propose Local-Guided Global Attention Self-Attention (LGGSA) for extracting global and local information. LGGSA computes localized attention through a sliding window to capture rich contextual details. These local features are then aggregated for global attention computation, enabling the model to focus on areas signified as important by local information. To address the problems of tiny defects and low background contrast, we enhance the learning process by adding supervision to the CNN branch, forcing the branch to learn detailed boundary information. In addition, to take full advantage of the different modeling potentials of CNN and Transformer, we designed the Cross-Branch Feature Interaction Module (CBFI), which achieves a deep interaction between the two features through correlation-weighted integration to optimize feature extraction and representation. Finally, the edge-guided decoder (EGD) utilizes the boundary information extracted by the CNN to guide feature fusion to compensate for the loss of detail information. Experimental results on three public defect datasets demonstrate that our method exhibits promising performance.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103099"},"PeriodicalIF":8.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129636","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":"EEG-based floor vibration serviceability evaluation using machine learning","authors":"Jiang Li ,&nbsp;Weizhao Tang ,&nbsp;Jiepeng Liu ,&nbsp;Yunfei Zhao ,&nbsp;Y.Frank Chen","doi":"10.1016/j.aei.2024.103089","DOIUrl":"10.1016/j.aei.2024.103089","url":null,"abstract":"<div><div>The emergence of novel floor systems has made the vibration serviceability evaluation, conducted through testing and finite element simulation, a time-consuming process that struggles to accurately reflect the user experience. Even when occupant feelings are taken into account, the evaluations may be subjective. This study introduced electroencephalogram (EEG) to grade the ranges of peak accelerations (ACCs) and maximum transient vibration values (MTVVs). To achieve this goal, a supervised learning algorithm, the eXtreme Gradient Boosting (XGBoost), were adopted to conduct binary classification to recognize the threshold of peak ACCs and MTVVs. Accelerometers and an EEG acquisition instrument were utilized to simultaneously capture the ACC dataset and the EEG of volunteers. Characteristic EEG time-domain curves were then obtained by averaging the preprocessed curves corresponding to specific ranges of peak ACCs. The vibration-induced event-related potential components, P50 (the positive potential component with 50 ms latency) and N200 (the negative potential component with 200 ms latency), were identified based on the averaged EEG curves. Additionally, the peak amplitude and peak latency were calculated using cognitive neuroscience methods. The study results suggest that the potential for floor vibration-induced components is around 10 μV. Lastly, XGBoost was utilized to identify the thresholds of different ranges of peak ACCs and MTVVs using EEG time-domain and frequency-domain features. The classification accuracy according to MTVV using XGBoost can reach up to 99 %. This study quantified human perception of floor vibrations based on EEG and optimized peak ACC and MTVV threshold for the cold-formed steel floor vibration serviceability evaluation.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103089"},"PeriodicalIF":8.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129699","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":"A fuzzy dematel-based delegated Proof-of-Stake consensus mechanism for medical model fusion on blockchain","authors":"Zhi Li ,&nbsp;Fuhe Liang ,&nbsp;Ming Li","doi":"10.1016/j.aei.2024.103095","DOIUrl":"10.1016/j.aei.2024.103095","url":null,"abstract":"<div><div>To ensure consensus regarding the contribution of distributed medical institutions to data models and the transformation of their application value, this paper proposes a fuzzy DEMATEL-based delegated proof-of-stake consensus mechanism for medical model fusion on blockchain. By utilizing transparent, verifiable consensus methods and monitorable on-chain distributed service logic, this framework determines the value-added performance and value-added application of distributed models. Considering that traditional consensus mechanisms are designed primarily for static, deterministic numerical data, they fall short in terms of accommodating consensus for dynamic, interval-based models. To address this limitation, we propose an enhancement to the DPOS consensus mechanism by using fuzzy DEMATEL. This approach enables contribution measurement and consensus for distributed models on the basis of interval-based model characteristics, thereby improving the interpretability of contribution assessments in medical institutions. Since the current lack of application paradigms for data models in distributed environments limits the value conversion of models at the application layer, we propose the construction of a distributed application logic using blockchain and smart contracts. By leveraging smart contracts to protect data privacy and model ownership, this approach enables the standardized and service-oriented transformation of application values. Finally, we conducted an experimental case study using a real medical image diagnostic model to verify and evaluate the feasibility and efficiency of the proposed framework, and a prototype system is established to demonstrate the distributed model consensus and service requirements when collaborating with companies in real-life scenarios. Four sets of experiments were conducted to ensure the feasibility and efficiency of both the distributed consensus and the distributed service process. The results indicate that the proposed consensus mechanism achieves distributed consensus with a latency of approximately 0.2853 s. While the proposed distributed service framework has disadvantages in terms of the throughput and average latency, the differences are minimal—only 0.3937 requests per second and 0.4060 s, respectively, compared with on-chain business creation. Additionally, compared with on-chain business creation, the framework increases CPU and memory utilization by just 15.8902% and 2.4697%, respectively.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103095"},"PeriodicalIF":8.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130279","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":"Quantitative recommendation of fault diagnosis algorithms based on multi-order random graph convolution under case-learning paradigm","authors":"Chen Lu ,&nbsp;Xinyu Zou ,&nbsp;Lulu Sun ,&nbsp;Zhengduo Zhao ,&nbsp;Laifa Tao ,&nbsp;Yu Ding ,&nbsp;Jian Ma","doi":"10.1016/j.aei.2025.103108","DOIUrl":"10.1016/j.aei.2025.103108","url":null,"abstract":"<div><div>The rapid development of intelligent algorithms has significantly expanded the range of algorithms available for Prognostics and Health Management (PHM). Selecting the appropriate algorithm for a specific task is crucial for effective PHM applications. Learning from past PHM cases is an effective way to automate algorithm recommendations, reducing reliance on expert experience. Human-AI collaboration provides new ideas for achieving this capability. However, in emerging fields or early-stage research, the limited number of cases—coupled with volatility and noise—often results in low recommendation accuracy and weak stability. To address this issue, we propose a multi-order random graph convolution network (MOR-GCN) within a case-learning paradigm. This method uses graphs to model and optimize case correlations, helping engineers narrow down algorithm choices to suitable candidates. We first develop a correlation modeling and optimization method based on a graph network, enhancing information aggregation between similar cases and reducing the impact of case noise on the recommendation model. Next, we design an ensemble recommender using MOR-GCN, which aggregates features of adjacent case nodes through a case correlation network graph (CCNG), further improving recommendation accuracy and stability through ensemble learning. Experimental results from a gearbox fault diagnosis case set demonstrate that the MOR-GCN model can automatically recommend fault diagnosis algorithms based on task attributes, achieving an average accuracy of 77.20 % for single recommendations and 89.90 % for fuzzy recommendations. This framework showcases the potential of artificial intelligence (AI) to assist human decision-making in PHM, minimizing the dependency on expert knowledge during the PHM design stage.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103108"},"PeriodicalIF":8.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129629","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":"Automatic identification of integrated construction elements using open-set object detection based on image and text modality fusion","authors":"Ruying Cai ,&nbsp;Zhigang Guo ,&nbsp;Xiangsheng Chen ,&nbsp;Jingru Li ,&nbsp;Yi Tan ,&nbsp;Jingyuan Tang","doi":"10.1016/j.aei.2024.103075","DOIUrl":"10.1016/j.aei.2024.103075","url":null,"abstract":"<div><div>The application of object detection technology in the field of construction safety contributes significantly to on-site safety management and has already shown considerable progress. However, current research primarily focuses on detecting pre-defined classes annotated within single datasets. In-depth research in construction safety requires the detection of all influencing factors related to construction safety. The emergence of large language models offers new possibilities, and multimodal models that combine these with computer vision technology could break through the existing limitations. Therefore, this paper proposes the Grounding DINO multimodal model for the automatic detection of integrated construction elements, enhancing construction safety. First, this study reviews the literature to collect relevant datasets, summarizes their characteristics, and processes the data, including the processing of annotation files and the integration of classes. Subsequently, the Grounding DINO model is constructed, encompassing image and text feature extraction and enhancement, and a cross-modal decoder that fuses image and text features. Multiple dataset experimental strategies are designed to validate Grounding DINO’s capabilities in continuous learning, with a unified class system created based on integrated classes for model detection input text prompts. Finally, experiments involving zero-shot and fine-tuning evaluations, continuous learning validation, and effectiveness testing are conducted. The experimental results demonstrate the generalization capability and potential for continuous learning of the multimodal model.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103075"},"PeriodicalIF":8.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129695","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":"You can be more trustworthy: A feature fusion reinforcement network for credible anti-noise fault diagnosis","authors":"Yuan Wei ,&nbsp;Hongchong Peng ,&nbsp;Mansong Rong ,&nbsp;Xiaohui Gu ,&nbsp;Xiangyan Chen","doi":"10.1016/j.aei.2024.103056","DOIUrl":"10.1016/j.aei.2024.103056","url":null,"abstract":"<div><div>Significant research progress has been made in intelligent fault diagnosis algorithms. However, these methods face challenges such as noise interference and untrustworthy diagnostic results in industrial practice, which limit their performance in practical applications. This paper proposes a new feature fusion and reinforcement network combined with swin-transformer (Swin-FFRN) for noise-resistant and trustworthy diagnosis, which combines a global feature extraction network and a staged convolutional fusion operation for fine-grained fault feature extraction and noise suppression in 2D time–frequency maps. The Swin-FFRN is used to analyze 2D time–frequency map data of different mechanical faults in a low signal-to-noise ratio environment by introducing a channel attention mechanism and a spatial attention mechanism to strengthen the critical fault features that are strongly correlated with the classification of the faults so that the model focuses on the crucial features. Moreover, the noise immunity performance is evaluated using the latest methods on two different datasets, and intuitive visual interpretability is provided to show model credibility. The results show that the noise-resistant diagnostic accuracy of the proposed method is improved by 5.43% on average with respect to the SOTA method. By enhancing the key input features, the proposed method can give diagnostic results with a reasonable decision basis.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103056"},"PeriodicalIF":8.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129697","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}