Advanced Engineering Informatics最新文献

{"title":"An optimization-based motion planner for dual-arm manipulation of the soft deformable linear objects with nonnegligible gravity","authors":"Shirui Wu,&nbsp;Jiwen Zhang,&nbsp;Dan Wu","doi":"10.1016/j.aei.2024.102874","DOIUrl":"10.1016/j.aei.2024.102874","url":null,"abstract":"<div><div>The dual-arm manipulation of deformable linear objects (DLOs) represents a practical and challenging problem in robotics research, offering significant potential for various industrial applications, including cable assembly. To accurately model the mechanical properties of DLOs, a Kirchhoff differential model is employed, which parameterizes the DLO configuration as a 6-dimensional manifold. Traditionally, approaches to solving this planning problem relied solely on sampling-based methods, incurring high computational costs due to the necessity of obtaining the DLO shape for each sample. Additionally, these methods completely ignored gravity, assuming that the DLO was stiff enough. However, in many industrial scenarios, this assumption cannot hold, particularly when dealing with soft DLOs, where the effects of gravity are non-negligible, leading to poorer stability and sensitivity. In this work, a novel optimization-based paradigm is proposed for the manipulation planning of soft DLOs with dual arms, addressing the challenges associated with their soft nature and the influence of gravity. The concept of ’stability distance’ is introduced as an easily measurable indicator of the degree of DLO stability. Furthermore, a thorough investigation into the singularity phenomenon in DLO local leading is conducted to identify its causes and provide effective solutions. Additionally, a strategy is introduced to avoid local traps of the DLO in complex obstacle environments. The comprehensive planner is validated through both simulation and hardware experiments, utilizing two types of soft DLOs with a length of approximately 1 m in various environmental settings. The results demonstrate the promising performance of the algorithm across diverse assembly scenarios.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102874"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446797","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 multi-sensor fused incremental detection model for blade crack with cross-attention mechanism and Dempster-Shafer evidence theory","authors":"Tianchi Ma ,&nbsp;Yuguang Fu","doi":"10.1016/j.aei.2024.102952","DOIUrl":"10.1016/j.aei.2024.102952","url":null,"abstract":"<div><div>Deep learning-based blade crack detection models work on the premise of a fixed data distribution, while the influx of new dataset for faults under blade crack propagation often leads to a catastrophic forgetting problem. Meanwhile, it is difficult for a single sensor to reflect the health status of the blade comprehensively under the limitation of installation location and coverage. To solve the above problems, a multi-sensor fused incremental detection model (MFIDM) for blade cracks with the cross-attention mechanism and the Dempster-Shafer evidence theory (DST) is proposed. Firstly, vibration signals of centrifugal fans are collected by multiple accelerometers deployed at different locations. Then, a two-branch feature fusion method based on the cross-attention mechanism is proposed to overcome the class imbalance due to the replay incremental learning method. After that, the fused features are fed into a Softmax classifier to complete the initial classification of blade status. Finally, a modified DST based on the cross-correlation energy is adopted for multi-sensor decision fusion to obtain the final blade crack detection results. The effectiveness of the proposed method is verified by two incremental blade crack datasets, and MFIDM achieves the better performance compared with other related incremental detection methods.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102952"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659052","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":"Ontology guided multi-level knowledge graph construction and its applications in blast furnace ironmaking process","authors":"Xiaoke Huang ,&nbsp;Chunjie Yang ,&nbsp;Yuyan Zhang ,&nbsp;Siwei Lou ,&nbsp;Liyuan Kong ,&nbsp;Heng Zhou","doi":"10.1016/j.aei.2024.102927","DOIUrl":"10.1016/j.aei.2024.102927","url":null,"abstract":"<div><div>Due to the widespread presence of knowledge in factories, integrating various types of knowledge to solve different tasks in industrial production processes, including prediction, diagnosis, and control tasks, is of great significance and challenging. Knowledge graph, as a method of knowledge representation, holds significant promise for addressing challenges within industrial contexts. However, current research on knowledge graph has a limitation in that task related knowledge graph focus on structure information, while ignoring semantic and logical information in knowledge. Additionally, the existing ontologies designed for industrial production lack adaptability to cater to the diverse needs of different industrial tasks. This paper proposes a multi-level knowledge graph defined by ontology to introduce semantics and further explore the methods for combining semantics to complete practical industrial tasks. To ensure the accurate sampling of heterogeneous nodes, four semantic templates are generated using if-then rule logic. Different kinds of neighbor nodes are defined through the if-then rule logic, leading to a accelerated generation of target subgraphs related to different tasks. In this way, the plant-wide distributed computing for fault diagnosis tasks can be easily realized. Furthermore, this paper introduces a framework for semantics extraction and graph embedding based on multi-information fusion. This framework integrates semantic information, structural information, and node attribute information within the graph to deliver a holistic feature representation for prediction and control tasks. We take blast furnace ironmaking process as an industrial case study and the experimental results demonstrate the crucial role of semantics in enhancing the knowledge expression capability of graphs. Based on the blast furnace simulation experiment platform, the proposed method achieves 92.76% accuracy in the blast furnace fault diagnosis task, and the diagnosis time is reduced by 58.44% compared with the traditional rule-based method. In the self-healing control task of the blast furnace, the proposed graph embedding method can achieve a complete control process in three types of blast furnace faults: blowing out, tuyere failure, and low stockline. The control effect can be comparable to manual operation.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102927"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659113","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":"Parallel GhostNet classification prediction method for supercapacitor remaining useful life prediction","authors":"Quan Lu,&nbsp;Wenju Ju,&nbsp;Linfei Yin","doi":"10.1016/j.aei.2024.102916","DOIUrl":"10.1016/j.aei.2024.102916","url":null,"abstract":"<div><div>Accurate and rapid prediction of supercapacitors’ remaining useful life (RUL) and timely replacement of failing supercapacitors are of great importance to systemic stability and safety. To decrease the effects of the manual extraction of aging characteristics and fluctuations in the capacity data of supercapacitors for supercapacitor RUL prediction, a parallel GhostNet classification prediction method for supercapacitor RUL prediction is proposed. In this study, the mapping relationship between supercapacitor charging/discharging capacity data and RUL is established directly. In addition, the aging characteristics are learned from the raw observation data without relevant reserve knowledge. The supercapacitor RUL is quantified into 30 rank intervals and predicted by the parallel GhostNet classification method. The validation results based on 60 supercapacitors indicate that the prediction precision of the parallel GhostNet for supercapacitor RUL is 81.84 %, 21.28 % higher than that of a single GhostNet, 19.86 % higher than that of the Xeption model with the highest accuracy among other classical networks. Furthermore, introducing depth separable convolution, the prediction speed of the proposed parallel GhostNet model is 50576 s faster than that of the Xeption model.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102916"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659192","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":"Enhancing EEG artifact removal through neural architecture search with large kernels","authors":"Le Wu ,&nbsp;Aiping Liu ,&nbsp;Chang Li ,&nbsp;Xun Chen","doi":"10.1016/j.aei.2024.102831","DOIUrl":"10.1016/j.aei.2024.102831","url":null,"abstract":"<div><div>Electroencephalography (EEG) stands as one of the most vital noninvasive tools in neuroscience and clinical practice. Nevertheless, EEG data is highly susceptible to interference from various artifacts, which, in turn, can severely impact the subsequent analyses. As a result, the removal of these unwanted artifacts is of utmost importance. Recently, deep learning methods have demonstrated superior performance in artifact removal compared to traditional approaches. However, experts often invest substantial time and effort in identifying an efficient architecture, a process that is time-consuming and labor-intensive. In light of this challenge, this study introduces, for the first time, an artifact removal method based on neural network architecture search. This approach assigns probabilities to each potential operation within the network and optimizes the most suitable architecture based on the characteristics of the input data. Additionally, we expand the search space by incorporating large convolutional kernels, enabling the network to encompass a wider receptive field for the more effective capture of inherent EEG characteristics. The proposed method is evaluated on publicly available datasets, including electromyography (EMG), electrooculogram (EOG), electrocardiogram (ECG), and motion artifacts. Our results demonstrate that architectures incorporating convolution operations with varied kernel scales and shortcut connections are particularly effective for artifact removal. Notably, our method outperforms state-of-the-art techniques, achieving an average correlation coefficient (CC) exceeding 0.95, a relative root mean squared error (RRMSE) below 0.3, and a signal-to-noise ratio (SNR) above 12 dB. These findings underscore the potential of our method as a reliable and advanced technique for EEG denoising.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102831"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359377","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 novel product shape design method integrating Kansei engineering and whale optimization algorithm","authors":"Xiang Zhao ,&nbsp;Sharul Azim Sharudin ,&nbsp;Han-Lu Lv","doi":"10.1016/j.aei.2024.102847","DOIUrl":"10.1016/j.aei.2024.102847","url":null,"abstract":"<div><div>The focus of consumer desire transitions from product functionality to emotional resonance in experience economy era, wherein emotional needs of users increasingly become a critical factor in product design. However, traditional approaches to product shape design often rely heavily on the designer’s intuition and experience, sometimes neglecting to incorporate emotional and humanistic elements into the product’s shape, thus resulting in inconsistencies in design results and quality. To address this challenge, this study introduces a novel method for emotionally driven product shape design that integrates Kansei engineering and the Whale Optimization Algorithm (WOA). This approach aims to fulfill consumer emotional demands related to product form and enhance overall user satisfaction. Firstly, the process utilized Python web crawlers to collect online product review texts and product images from e-commerce platforms. Next, Latent Dirichlet Allocation (LDA) and Analytic Hierarchy Process (AHP) were employed to extract representative emotional vocabularies, while representative samples were defined and deconstructed through clustering and morphological analysis. Then, semantic Differential (SD) questionnaires were distributed to collect consumer evaluations of product shape imagery, leading to the development of a user emotional prediction model for product shape. Then, WOA was introduced to optimize the performance of Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) models. Finally, Particle Swarm Optimization (PSO) and Seagull Optimization Algorithm (SOA) were employed to improve the prediction model, and the effect of these models was compared by the error method. This analysis explored the accuracy of these non-linear models in order to identify the optimal model for application in product form design cases. The scientific validity and effectiveness of this method were demonstrated utilizing whiskey bottle shape design as a case study. The results exhibit that under the WOA-BPNN model, the average error rates for four sets of perceptual words were 3.08%, 2.60%, 6.53%, and 5.70%, respectively. The WOA-based BPNN model outperformed other models in predictive ability, suggesting its utility as a valuable tool for designers during the front-end development stage of emotionally driven product form design.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102847"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417044","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":"Dynamic flexible job-shop scheduling by multi-agent reinforcement learning with reward-shaping","authors":"Lixiang Zhang ,&nbsp;Yan Yan ,&nbsp;Chen Yang ,&nbsp;Yaoguang Hu","doi":"10.1016/j.aei.2024.102872","DOIUrl":"10.1016/j.aei.2024.102872","url":null,"abstract":"<div><div>Achieving mass personalization presents significant challenges in performance and adaptability when solving dynamic flexible job-shop scheduling problems (DFJSP). Previous studies have struggled to achieve high performance in variable contexts. To tackle this challenge, this paper introduces a novel scheduling strategy founded on heterogeneous multi-agent reinforcement learning. This strategy facilitates centralized optimization and decentralized decision-making through collaboration among job and machine agents while employing historical experiences to support data-driven learning. The DFJSP with transportation time is initially formulated as heterogeneous multi-agent partial observation Markov Decision Processes. This formulation outlines the interactions between decision-making agents and the environment, incorporating a reward-shaping mechanism aimed at organizing job and machine agents to minimize the weighted tardiness of dynamic jobs. Then, we develop a dueling double deep Q-network algorithm incorporating the reward-shaping mechanism to ascertain the optimal strategies for machine allocation and job sequencing in DFJSP. This approach addresses the sparse reward issue and accelerates the learning process. Finally, the efficiency of the proposed method is verified and validated through numerical experiments, which demonstrate its superiority in reducing the weighted tardiness of dynamic jobs when compared to state-of-the-art baselines. The proposed method exhibits remarkable adaptability in encountering new scenarios, underscoring the benefits of adopting a heterogeneous multi-agent reinforcement learning-based scheduling approach in navigating dynamic and flexible challenges.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102872"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446798","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":"SparseDet: Towards efficient multi-view 3D object detection via sparse scene representation","authors":"Jingzhong Li,&nbsp;Lin Yang,&nbsp;Zhen Shi,&nbsp;Yuxuan Chen,&nbsp;Yue Jin,&nbsp;Kanta Akiyama,&nbsp;Anze Xu","doi":"10.1016/j.aei.2024.102955","DOIUrl":"10.1016/j.aei.2024.102955","url":null,"abstract":"<div><div>Efficient and reliable 3D object detection via multi-view cameras is pivotal for improving the safety and facilitating the cost-effective deployment of autonomous driving systems. However, owing to the learning of <em>dense</em> scene representations, existing methods still suffer from high computational costs and excessive noise, affecting the efficiency and accuracy of the inference process. To overcome this challenge, we propose SparseDet, a model that exploits <em>sparse</em> scene representations. Specifically, a sparse sampling module with category-aware and geometry-aware supervision is first introduced to adaptively sample foreground features at both semantic and instance levels. Additionally, to conserve computational resources while retaining context information, we propose a background aggregation module designed to compress extensive background features into a compact set. These strategies can markedly diminish feature volume while preserving essential information to boost computational efficiency without compromising accuracy. Due to the efficient sparse scene representation, our SparseDet achieves leading performance on the widely used nuScenes benchmark. Comprehensive experiments validate that SparseDet surpasses the PETR while reducing the decoder computational complexity by 47% in terms of FLOPs, facilitating a leading inference speed of 35.6 FPS on a single RTX3090 GPU.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102955"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701239","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":"Health indicator adaptive construction method of rotating machinery under variable working conditions based on spatiotemporal fusion autoencoder","authors":"Yong Duan,&nbsp;Xiangang Cao,&nbsp;Jiangbin Zhao,&nbsp;Man Li,&nbsp;Xin Yang,&nbsp;Fuyuan Zhao,&nbsp;Xinyuan Zhang","doi":"10.1016/j.aei.2024.102945","DOIUrl":"10.1016/j.aei.2024.102945","url":null,"abstract":"<div><div>Health indicators (HI) can effectively reveal potential faults and express the degradation process of rotating machinery in engineering, which are significant for health state assessment, prognostic and decision-making. Nevertheless, most classical HI construction methods have some problems of inadequate spatiotemporal feature extraction, neglect of working conditions and individual discrepancy, and difficulty in adapting to complex degradation, leading to poor model feature expression and adaptability. To overcome these challenges, this paper proposes a new HI adaptive construction method of rotating machinery (HCPTSCAE). A spatiotemporal fusion autoencoder neural network integrating pyramid convolution and Transformer is proposed to extract the signal’s deep spatiotemporal degradation features. Then, condition domain alignment and individual degradation alignment are introduced as homogeneity constraints to reduce the discrepancy of conditions and individuals. On this basis, an autoencoder structure with adaptive weight is used to adjust the model and automatically construct HI based on the quadratic function degradation rule. The effectiveness and applicability of the HCPTSCAE network are validated by the Xi’an Jiaotong University (XJTU) bearing degradation dataset and our lab’s reducer dataset. The mean comprehensive score for different bearings is 0.7283, showing an average increase of 0.2026 compared with other methods. The mean comprehensive score for different reducers is 0.6680, with an average increase of 0.1664 compared with other methods. Moreover, the results indicate that HCPTSCAE has advantages in finding the early state degradation point and predicting remaining useful life, which promotes the trend consistency of the samples’ same features.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102945"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701269","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":"Physics-informed and data-driven hybrid method for transmission accuracy design optimization of planetary roller screw mechanism","authors":"Genshen Liu ,&nbsp;Peitang Wei ,&nbsp;Xuesong Du ,&nbsp;Siqi Liu ,&nbsp;Li Luo ,&nbsp;Rui Hu ,&nbsp;Caichao Zhu ,&nbsp;Jigui Zheng ,&nbsp;Pengliang Zhou","doi":"10.1016/j.aei.2024.102883","DOIUrl":"10.1016/j.aei.2024.102883","url":null,"abstract":"<div><div>The planetary roller screw mechanism (PRSM) faces an ever-increasing precision transmission demand in current advanced equipment. The relationship between machining errors and transmission accuracy remains elusive due to the over-simplified physical models and small-sample experimental datasets. This work proposes a physics-informed and data-driven hybrid strategy for PRSM transmission accuracy evaluation and tolerance optimization. In the physical model, a PRSM transmission accuracy model is developed to calculate transmission error that considers 16 machining errors in eccentric, nominal diameter, pitch, flank angle, and roller consistency. In the dataset establishment, thread profile measurements and dynamic leadscrew inspections are conducted for the machining error and transmission accuracy data acquisition. A data augmentation approach combining the physical model with the generative adversarial network is utilized to predict travel deviation, variations, and axial backlash and estimate machining error contribution with the small-sample experimental dataset. It is firstly found that the roller consistency of nominal diameter significantly affects PRSM travel variation <em>V</em>_2π with a 17.3 % importance value. With the developed framework, the key tolerances for screw, roller, nut, and roller consistency are optimized toward a typical precision transmission requirement using the non-dominated sorting genetic algorithm. It also provides a tolerance grade recommendation table with PRSM transmission accuracy level in engineering practice.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102883"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529670","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}