Advanced Engineering Informatics最新文献

{"title":"Advancements in 3D digital model generation for digital twins in industrial environments: Knowledge gaps and future directions","authors":"Masoud Kamali,&nbsp;Behnam Atazadeh,&nbsp;Abbas Rajabifard,&nbsp;Yiqun Chen","doi":"10.1016/j.aei.2024.102929","DOIUrl":"10.1016/j.aei.2024.102929","url":null,"abstract":"<div><div>Digital twins are considered a transformative paradigm for industrial environments, providing a dynamic, digital, and intelligent representation of industrial assets. The necessity of digital twins in industrial settings is underscored by their ability to enhance asset monitoring, operational efficiency, and maintenance activities. The 3D digital model is fundamental for digital twins, serving not only as a digital representation of industrial environment but also facilitating the simulation of real-world scenarios. Although there have been extensive studies on the application of digital twins in industrial environments, the creation of 3D digital model for digital twins in existing industrial environments is still overlooked, primarily due to the complexity of these environments. This article aims to propose a workflow to create a 3D digital model for digital twins in existing industrial environments that includes four key components: 1) Data capturing, 2) 3D modeling, 3) Asset localization, and 4) Information integration. A significant body of literature on each component is surveyed to identify current knowledge gaps in harnessing 3D digital models for digital twins in industrial environments. In response to these gaps, this study proposes a series of future research directions, including automated data validation, real-time processing, semi-supervised or unsupervised learning-based 3D reconstruction methods, and 3D visualization approaches for industrial assets.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102929"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid postearthquake modelling method for deformation monitoring models of high arch dams based on metalearning and graph attention","authors":"Jichen Tian ,&nbsp;Yonghua Luo ,&nbsp;Huibao Huang ,&nbsp;Jiankang Chen ,&nbsp;Yanling Li","doi":"10.1016/j.aei.2024.102925","DOIUrl":"10.1016/j.aei.2024.102925","url":null,"abstract":"<div><div>Southwest China is the world’s most densely populated area for high dams over 200 m and is also a region with high seismic activity. Earthquakes can significantly alter dam structures, resulting in substantial discrepancies between preearthquake and postearthquake deformation monitoring data. Deformation is a critical indicator of the structural response of dams to internal and external environmental factors. Establishing a dam deformation structural health monitoring (SHM) model promptly after an earthquake is crucial for postearthquake structural health analysis and preventing major accidents. In this paper, we propose a rapid modelling method for postearthquake deformation SHM of high arch dams that uses metalearning and graph attention techniques. First, we develop an SHM model tailored for postseismic small-sample data modelling, integrating a multihead attention mechanism with hydraulic-temporal graph feature fusion. On this basis, we introduce a metalearning framework to derive the initial model parameters from preearthquake data. The proposed model is applied to vertical radial deformation monitoring of the world’s only 200-metre-high arch dam subjected to strong near-field earthquakes. The effectiveness of our metalearning framework for postearthquake data is validated by comparing it with the transfer learning framework. Through a comparison with nine baseline models across six postearthquake modelling scenarios, we demonstrate that the proposed model achieves the highest accuracy and exhibits unique engineering applicability for rapid postearthquake modelling tasks. Ablation experiments further confirm the effectiveness of the proposed modules.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102925"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659110","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":"Multivariable real-time prediction method of tunnel boring machine operating parameters based on spatio-temporal feature fusion","authors":"Shilong Pang ,&nbsp;Weihua Hua ,&nbsp;Wei Fu ,&nbsp;Xiuguo Liu ,&nbsp;Xin Ni","doi":"10.1016/j.aei.2024.102924","DOIUrl":"10.1016/j.aei.2024.102924","url":null,"abstract":"<div><div>The tunnel boring machine (TBM) is an important piece of equipment in tunnelling. Accurate prediction of its operating parameters is essential for the operator to adjust the tunnelling strategy in time. Based on the data of a tunnel project in western Sichuan, and considering the easy accessibility of the parameters, this study selects four operational parameters closely related to the tunnelling process as research objects, namely cutterhead speed, total thrust, penetration, and cutterhead torque. A new multi-attention mechanism fusion neural network (TBMformer) based on spatio-temporal feature fusion is proposed. Firstly, based on the establishment of a function to eliminate invalid data to identify different operating states of the TBM. Then the abnormal data were excluded using the isolated forest algorithm, followed by data noise reduction using the Kalman filter, and finally a high-quality TBM dataset was obtained. Secondly, in order to take into account the influence of the TBM real-time running time on the running state of TBM equipment, the correlation between different tunnelling circles and the correlation between different parameters, the time information and ring number information are encoded, and the time attention mechanism and self-attention mechanism are introduced in the time domain and space domain, respectively. In parallel, we employ LSTM to capture the long-term dependencies within TBM sequences. Finally, based on the Informer model, a variety of attention mechanisms are integrated to form the TBMformer model that can deal with the multi-variable real-time prediction of TBM operating parameters. In this study, three datasets with varying spatial resolutions were generated for experimental and analytical purposes, utilising tunnel construction data from two distinct geological contexts in western Sichuan and northern China. The TBMformer model exhibits superior predictive accuracy, with an average accuracy (ACC) of over 94.3% on the three test sets, in comparison to other data-driven methods. The results show that this method can provide real-time guidance to the operator, thereby reducing uncertainty in the control of TBM equipment.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102924"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659119","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":"Risk assessment and interactive motion planning with visual occlusion using graph attention networks and reinforcement learning","authors":"Xiaohui Hou ,&nbsp;Minggang Gan ,&nbsp;Wei Wu ,&nbsp;Tiantong Zhao ,&nbsp;Jie Chen","doi":"10.1016/j.aei.2024.102941","DOIUrl":"10.1016/j.aei.2024.102941","url":null,"abstract":"<div><div>This study proposes an innovative framework that integrates risk assessment and interactive planning for autonomous vehicles (AVs) navigating unprotected left turns at occluded intersections. The upper risk assessment module of this framework synergizes Expert-Informed Graph Attention Networks (EIGAT) with Mixture Density Network (MDN) to predict the probabilistic distributions of the potential risk of the occluded zone. And the lower interactive planning module, utilizing Adaptive Loss Enhanced Reinforcement Learning (ALERL), further develops an interactive policy that integrates additional considerations for prediction accuracy of blind zones, potential risk measure of conditional value at risk (CVaR), and encourage of exploratory interaction. Simulation tests are conducted in occluded intersection scenarios with various traffic density level. Both qualitative and quantitative performance validate the effectiveness and adaptability of our proposed controller in risk assessment and interactive planning for AVs compared with other baseline methods.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102941"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659121","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 adaptive melody search algorithm based on low-level heuristics for material feeding scheduling optimization in a hybrid kitting system","authors":"Yufan Huang,&nbsp;Lingwei Zhao,&nbsp;Binghai Zhou","doi":"10.1016/j.aei.2024.102855","DOIUrl":"10.1016/j.aei.2024.102855","url":null,"abstract":"<div><div>Facing highly diversified market demands in automotive industry, changing variants of components produced in mixed-model assembly lines (MMALs) has led to an increasing attention towards the material-feeding processes. Therefore, this paper originally proposes a novel type of material-feeding mode called hybrid kitting, leading to a better adaptation to MMALs. Since energy-saving and Just-in-time (JIT) principles are the two major concerns in production systems, a bi-objective mathematical model is established aiming to collaboratively minimize the multi-load automated guided vehicle (AGV) energy consumption as well as the kit conveyor depreciation cost in the hybrid kitting-based material-feeding system. Due to the non-deterministic polynomial hard (NP-hard) nature of the problem, a modified melody search-based hyper-heuristic algorithm (MMSA-HH) is proposed with seven low-level heuristic (LLH) operators. Based on the basic MSA, the melody composition rules are redesigned to enrich the diversity of solutions, adaptive adjustment of parameters is used to balance the local search and global search, and the fluctuated crowding distance calculation method is used in elite selection along with Pareto rank calculation. Computational experiment results reveal the effectiveness of the MMSA-HH when solving the problem. Finally, the managerial insights are given through comparing the impacts of kit container size, AGV type, and different kitting modes on the two objectives.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102855"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417123","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":"SIMTSeg: A self-supervised multivariate time series segmentation method with periodic subspace projection and reverse diffusion for industrial process","authors":"Xiangyu Bao,&nbsp;Yu Zheng,&nbsp;Jingshu Zhong,&nbsp;Liang Chen","doi":"10.1016/j.aei.2024.102859","DOIUrl":"10.1016/j.aei.2024.102859","url":null,"abstract":"<div><div>Subsequences with varied regimes in the industrial multivariate time series (MTS) are closely associated with the dynamic status of the multi-phased industrial process. Time series segmentation (TSS) provides insights into the underlying behavior of industrial systems. However, the complexity of industrial data poses significant challenges to the conventional TSS methods. Motivated by this, a Self-supervised Industrial Multivariate Time-series Segmentation method (SIMTSeg) is presented in this work. An MTS folding module based on Ramanujan periodic subspace projection is first proposed, where the MTS is reshaped into the 3D feature map to realize the compact representation of the intricate data dependencies. Subsequently, a self-supervised module based on the encoder-decoder architecture is adopted to address the problem of deficient and task-specific annotations in industrial data. The folded feature map is denoised step by step following the reverse diffusion process, and finally turns into the segmentation mask without redundant details. The proposed SIMTSeg has been validated by a popular industrial benchmark, the Tennessee Eastman Process, and outperforms the unsupervised data-driven baselines in terms of various performance metrics. SIMTSeg has no prerequisite on the number of segmentation points or regime types, and is capable of giving more meaningful segmentation results that are in line with the high-level semantics.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102859"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417209","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":"Improving efficiency in structural optimization using RBFNN and MMA-Adam hybrid method","authors":"Kangjie Li,&nbsp;Wenjing Ye","doi":"10.1016/j.aei.2024.102869","DOIUrl":"10.1016/j.aei.2024.102869","url":null,"abstract":"<div><div>A significant challenge in traditional topology optimization (TO) methods lies in their low efficiency when handling large-scale design problems, primarily due to repetitive high-dimensional computations. Recently, the fusion of implicit neural representation with conventional structural topology optimization techniques has garnered considerable interest, owing to its various advantages, including the elimination of the need for filters. While this approach can yield design solutions comparable to those from traditional TO methods, it does not lead to clear efficiency gains; in some cases, the number of forward calculations is higher than in traditional methods. This study aims to enhance method efficiency by utilizing the radial basis function neural network (RBFNN) to implicitly represent the structure. Specifically, a set of trainable radial bases shapes and positions is employed to span the structure’s density field. Additionally, a hybrid approach is proposed, combining the method of moving asymptotes (MMA) with the Adam optimizer to update the neural network parameters. This updating technique accelerates convergence and enhances overall efficiency. Through adapting the bases via backpropagation and minimizing the loss function constructed based on traditional TO methods, our approach facilitates achieving design solutions with similar performance but with significantly fewer design variables and performance evaluations compared to traditional TO methods.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102869"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442570","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":"CWPR: An optimized transformer-based model for construction worker pose estimation on construction robots","authors":"Jiakai Zhou ,&nbsp;Wanlin Zhou ,&nbsp;Yang Wang","doi":"10.1016/j.aei.2024.102894","DOIUrl":"10.1016/j.aei.2024.102894","url":null,"abstract":"<div><div>Estimating construction workers’ poses is critically important for recognizing unsafe behaviors, conducting ergonomic analyses, and assessing productivity. Recently, utilizing construction robots to capture RGB images for pose estimation offers flexible monitoring perspectives and timely interventions. However, existing multi-human pose estimation (MHPE) methods struggle to balance accuracy and speed, making them unsuitable for real-time applications on construction robots. This paper introduces the Construction Worker Pose Recognizer (CWPR), an optimized Transformer-based MHPE model tailored for construction robots. Specifically, CWPR utilizes a lightweight encoder equipped with a multi-scale feature fusion module to enhance operational speed. Then, an Intersection over Union (IoU)-aware query selection strategy is employed to provide high-quality initial queries for the hybrid decoder, significantly improving performance. Besides, a decoder denoising module is used to incorporate noisy ground truth into the decoder, mitigating sample imbalance and further improving accuracy. Additionally, the Construction Worker Pose and Action (CWPA) dataset is collected from 154 videos captured in real construction scenarios. The dataset is annotated for different tasks: a pose benchmark for MHPE and an action benchmark for action recognition. Experiments demonstrate that CWPR achieves top-level accuracy and the fastest inference speed, attaining 68.1 Average Precision (AP) with a processing time of 26 ms on the COCO test set and 76.2 AP with 21 ms on the CWPA pose benchmark. Moreover, when integrated with the action recognition method ST-GCN on construction robot hardware, CWPR achieves 78.7 AP and a processing time of 19 ms on the CWPA action benchmark, validating its effectiveness for practical deployment.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102894"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532237","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":"Web-based human-robot collaboration digital twin management and control system","authors":"Xin Liu ,&nbsp;Gongfa Li ,&nbsp;Feng Xiang ,&nbsp;Bo Tao ,&nbsp;Guozhang Jiang","doi":"10.1016/j.aei.2024.102907","DOIUrl":"10.1016/j.aei.2024.102907","url":null,"abstract":"<div><div>Human-robot collaboration presents a promising application in customized production. The utilization of digital twin technology, an advanced form of virtual reality interaction, is pivotal in augmenting the capabilities of human-robot collaboration by enhancing perception and interaction dynamics. Human-robot collaboration digital twins have been proposed to create collaboration strategies, simulate collaborative processes, and guarantee individual safety. Nevertheless, there remain obstacles in the implementation of digital twins within human-robot collaboration. One notable challenge involves developing a user-friendly and easily accessible digital twin management and control system that is well-suited for human-robot collaboration. The human-robot collaboration digital twin is a sophisticated system that encompasses both physical and virtual environments. Effectively managing diverse resources within this system, devising collaboration strategies, and overseeing the collaboration process is essential for the standardized utilization of digital twins in human-robot collaboration. In response to the specific requirements of digital twins in this context, a web-based system has been conceptualized and implemented for managing and controlling human-robot collaboration digital twins. The system is structured based on the browser/server architecture model. Its functional design is implemented through a modular approach. The server-side database management system chosen is MySQL, while the programming and development of the control system are carried out using the Microsoft.Net Framework 4.8 on the Visual Studio 2022 platform. The system’s functional modules are exemplified through a case study on human-robot collaborative assembly. The findings of this study offer insights that can guide the standardized management and application of digital twins within the realm of human-robot collaboration.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102907"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532093","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 model-driven dual-derivation framework for quantitative fault detection in satellite power system","authors":"Pengming Wang ,&nbsp;Liansheng Liu ,&nbsp;Yuchen Song ,&nbsp;Zhidong Li ,&nbsp;Datong Liu","doi":"10.1016/j.aei.2024.102896","DOIUrl":"10.1016/j.aei.2024.102896","url":null,"abstract":"<div><div>Satellite power system (SPS) fault detection is of great significance to ensure the safety and stability of satellites. On-orbit SPS can divide 11 near-mutation operating conditions (OCs) in 4 types of sunlight regions. Combined with limited fault samples and high-dimensional, coupled, and noisy telemetry data, the accuracy of data- or knowledge-driven SPS fault detection is poor. Therefore, this work first comprehensively considers the mechanism model of SPS and the quantitative analysis results of corresponding faults, based on which an SPS fault behavior model is configured. By combining specific driving and parameter updating methods, strong support is provided for on-orbit SPS digital twin and fault detection. Then, a model-driven dual-derivation quantitative fault detection framework that combines accuracy and robustness is proposed. To be specific, an adaptive integral residual (AIR) algorithm for constructing SPS OCs is developed, which combines telemetry data with twin data to determine fault states and obtain fault information. Using the tree-structured Parzen estimator (TPE), iteratively adjust the model’s failure modes and parameters to obtain simulated data for the current fault. By comparing it with fault telemetry data, determine whether the current failure modes and parameters meet the requirements of quantitative fault detection. Finally, a semi-physical experimental platform was established, and experimental results confirmed the framework’s capability to accurately differentiate between different levels of faults. Specifically, the quantitative detection accuracy for typical faults reached 100%. Additionally, we designed seven accuracy and robustness indicators, all of which yielded optimal results when compared with common methods. Through experimental analysis of search space optimization methods, the universality of optimization methods has been demonstrated.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102896"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552955","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}