Advanced Engineering Informatics最新文献_第7页

Sliding time–frequency synchronous average based on autocorrelation function for extracting fault feature of bearings 基于自相关函数的滑动时频同步平均法提取轴承故障特征

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102876

Tao Liu , Laixing Li , Yongbo Li , Khandaker Noman

Making weak repetitive pulses clearly appear in time–frequency distribution is essential for detecting early failure of bearings. However, this operation is a challenging issue in fault diagnosis. To resolve this problem, a signal enhancement method called sliding time–frequency synchronous average based on autocorrelation function (STFSA-ACF) is proposed in this paper, based on three ways of signal enhancement. In the method, the autocorrelation function is first utilized to enhance the repetitive components of signals. The time–frequency representation of the autocorrelation function result is obtained by short-time Fourier transform. Furthermore, an improved version of time synchronous average called the sliding time–frequency synchronous average is developed to make the weak repetitive pulses more visible. In this method, a window sliding in the time–frequency plane is introduced to intercept the signal, and the time synchronous average is employed to process the intercepted section. The aforementioned operations construct the STFSA-ACF. Finally, the gamma transform is used to improve the contrast of generated STFSA-ACF. A series of numerically simulated signals are generated to validate the proposed algorithm. Besides, this method is employed to process part signals of two sets of public data. Performance of the proposed STFSA-ACF has been compared with popular methods such as fast Kurtogram, maximum correlated kurtosis deconvolution, and adaptive maximum second-order cyclostationarity blind deconvolution. Comparison results indicate that the STFSA-ACF has the best performance in terms of making weak repetitive pulses more visible.

在时频分布中清晰显示微弱的重复脉冲对于检测轴承的早期故障至关重要。然而，这一操作在故障诊断中是一个具有挑战性的问题。为解决这一问题，本文提出了一种基于自相关函数的滑动时频同步平均（STFSA-ACF）信号增强方法，该方法基于三种信号增强方式。在该方法中，首先利用自相关函数来增强信号的重复分量。通过短时傅里叶变换获得自相关函数结果的时频表示。此外，还开发了一种名为滑动时频同步平均的时间同步平均改进版，使微弱的重复脉冲更加明显。在这种方法中，引入了一个在时频平面上滑动的窗口来截取信号，并采用时间同步平均来处理截取的部分。上述操作构建出 STFSA-ACF。最后，使用伽马变换来提高生成的 STFSA-ACF 的对比度。为了验证所提出的算法，我们生成了一系列数值模拟信号。此外，该方法还被用于处理两组公共数据的部分信号。将所提出的 STFSA-ACF 的性能与快速 Kurtogram、最大相关峰度解卷积和自适应最大二阶回旋盲解卷积等常用方法进行了比较。比较结果表明，STFSA-ACF 在使微弱的重复脉冲更加明显方面表现最佳。

{"title":"Sliding time–frequency synchronous average based on autocorrelation function for extracting fault feature of bearings","authors":"Tao Liu , Laixing Li , Yongbo Li , Khandaker Noman","doi":"10.1016/j.aei.2024.102876","DOIUrl":"10.1016/j.aei.2024.102876","url":null,"abstract":"<div><div>Making weak repetitive pulses clearly appear in time–frequency distribution is essential for detecting early failure of bearings. However, this operation is a challenging issue in fault diagnosis. To resolve this problem, a signal enhancement method called sliding time–frequency synchronous average based on autocorrelation function (STFSA-ACF) is proposed in this paper, based on three ways of signal enhancement. In the method, the autocorrelation function is first utilized to enhance the repetitive components of signals. The time–frequency representation of the autocorrelation function result is obtained by short-time Fourier transform. Furthermore, an improved version of time synchronous average called the sliding time–frequency synchronous average is developed to make the weak repetitive pulses more visible. In this method, a window sliding in the time–frequency plane is introduced to intercept the signal, and the time synchronous average is employed to process the intercepted section. The aforementioned operations construct the STFSA-ACF. Finally, the gamma transform is used to improve the contrast of generated STFSA-ACF. A series of numerically simulated signals are generated to validate the proposed algorithm. Besides, this method is employed to process part signals of two sets of public data. Performance of the proposed STFSA-ACF has been compared with popular methods such as fast Kurtogram, maximum correlated kurtosis deconvolution, and adaptive maximum second-order cyclostationarity blind deconvolution. Comparison results indicate that the STFSA-ACF has the best performance in terms of making weak repetitive pulses more visible.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102876"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552952","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}

引用次数: 0

Spatially embedded transformer: A point cloud deep learning model for aero-engine coaxiality prediction based on virtual measurement 空间嵌入式变压器：基于虚拟测量的航空发动机同轴度预测点云深度学习模型

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102900

Tianyi Wu , Ke Shang , Xin Jin , Zhijing Zhang , Chaojiang Li , Steven Wang , Jun Liu

Coaxiality is a critical indicator of assembly accuracy in aero-engines, directly impacting the device’s operational performance and lifespan. Due to the enclosed nature of the aero-engine casing system, measuring the coaxiality of assembled components presents significant challenges. This paper introduces a novel deep learning architecture, the spatially embedded transformer (SETrans), designed to predict coaxiality from unassembled part data by correlating it with the contact surface points of assembled components. Additionally, a virtual measurement model is developed to collect micron-scale point cloud data, facilitating the fine-tuning of the deep learning model. The SETrans utilizes the transformer’s capability for global information aggregation to process point cloud inputs, capturing the comprehensive relationships across assembled surfaces. A newly designed module, the spatial bias, integrates distance and angular information between neighboring point clouds into the transformer block, enhancing the model’s ability to capture fine-grained local details. Experimental validation is conducted using two distinct datasets representing different assembly scenarios: the aero-engine casing, sampled using contact-based coordinate measuring machines, and the rotor, sampled using non-contact optical gaging products. These specific sampling methods test the generalizability of the SETrans across diverse measurement techniques. Comparative analysis with other point cloud deep learning benchmarks shows that the proposed approach achieves top prediction accuracies of 93.65% and 94.31% with a coaxiality precision of 0.01 mm across different data domains. These results confirm the effectiveness of the SETrans and demonstrate its adaptability to real-world assembly conditions involving various components.

同轴度是航空发动机装配精度的关键指标，直接影响设备的运行性能和使用寿命。由于航空发动机外壳系统的封闭性，测量组装组件的同轴度面临巨大挑战。本文介绍了一种新颖的深度学习架构--空间嵌入式变压器（SETrans），旨在通过将未组装部件数据与已组装部件的接触表面点相关联，预测未组装部件的同轴度。此外，还开发了一个虚拟测量模型来收集微米级的点云数据，以促进深度学习模型的微调。SETrans 利用变压器的全局信息聚合能力来处理点云输入，从而捕捉整个装配表面的综合关系。新设计的空间偏置模块将相邻点云之间的距离和角度信息整合到变压器模块中，增强了模型捕捉细粒度局部细节的能力。实验验证使用了代表不同装配场景的两个不同数据集：使用接触式坐标测量机采样的航空发动机外壳和使用非接触式光学测量产品采样的转子。这些特定的采样方法检验了 SETrans 在不同测量技术中的通用性。与其他点云深度学习基准的比较分析表明，所提出的方法在不同数据域的同轴度精度为 0.01 毫米的情况下，预测精度分别达到 93.65% 和 94.31%。这些结果证实了 SETrans 的有效性，并证明了它对现实世界中涉及各种组件的装配条件的适应性。

{"title":"Spatially embedded transformer: A point cloud deep learning model for aero-engine coaxiality prediction based on virtual measurement","authors":"Tianyi Wu , Ke Shang , Xin Jin , Zhijing Zhang , Chaojiang Li , Steven Wang , Jun Liu","doi":"10.1016/j.aei.2024.102900","DOIUrl":"10.1016/j.aei.2024.102900","url":null,"abstract":"<div><div>Coaxiality is a critical indicator of assembly accuracy in aero-engines, directly impacting the device’s operational performance and lifespan. Due to the enclosed nature of the aero-engine casing system, measuring the coaxiality of assembled components presents significant challenges. This paper introduces a novel deep learning architecture, the spatially embedded transformer (SETrans), designed to predict coaxiality from unassembled part data by correlating it with the contact surface points of assembled components. Additionally, a virtual measurement model is developed to collect micron-scale point cloud data, facilitating the fine-tuning of the deep learning model. The SETrans utilizes the transformer’s capability for global information aggregation to process point cloud inputs, capturing the comprehensive relationships across assembled surfaces. A newly designed module, the spatial bias, integrates distance and angular information between neighboring point clouds into the transformer block, enhancing the model’s ability to capture fine-grained local details. Experimental validation is conducted using two distinct datasets representing different assembly scenarios: the aero-engine casing, sampled using contact-based coordinate measuring machines, and the rotor, sampled using non-contact optical gaging products. These specific sampling methods test the generalizability of the SETrans across diverse measurement techniques. Comparative analysis with other point cloud deep learning benchmarks shows that the proposed approach achieves top prediction accuracies of 93.65% and 94.31% with a coaxiality precision of 0.01 mm across different data domains. These results confirm the effectiveness of the SETrans and demonstrate its adaptability to real-world assembly conditions involving various components.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102900"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552953","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}

引用次数: 0

Augmenting human-guided progressive learning with machine vision systems for robust surface defect detection 利用机器视觉系统增强人类引导的渐进式学习，实现稳健的表面缺陷检测

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102906

Swarit Anand Singh, Sahil J Choudhari, K.A. Desai

Machine vision systems commonly utilize Convolutional Neural Networks (CNNs) for in-line surface defect detection of manufactured components. The prediction abilities of vision-based inspection systems deteriorate with time as the defect detection model trained on fixed image datasets fails to accommodate deviations. This paper proposes a human-guided progressive learning approach that systematically imparts learning of new features to the CNN-powered vision-based defect detection system. The approach augments the surface defect detection model with human intelligence, using an intuitive user interface to address model drift. The human expert monitors the trained model performance under specific conditions leading to the change of characteristics during implementation, identifies misclassifications, and initiates re-training. The algorithm accumulates misclassified data till a pre-defined threshold level is reached or a human expert terminates inspection. The misclassified results merge with the original datasets for progressive re-training using a strategy similar to the base model development. The present work utilizes pre-trained CNN Efficientnet-b0 to develop the surface defect detection model for tapered roller inspection through transfer learning. It is concluded that the progressive re-training improves defect detection performance and reduces misclassifications. The Matthews Correlation Coefficient (MCC) score, derived from the confusion matrix, showed improvement from 0.6 to 0.82 after four iterations. A cross-model benchmarking study is also performed to show the versatility of the proposed approach. The present work demonstrated that the human-guided progressive learning approach can provide adaptability to vision-based surface defect detection utilizing deep learning algorithms and enhance system performance during real-time implementation.

机器视觉系统通常利用卷积神经网络（CNN）对制造部件进行在线表面缺陷检测。基于视觉的检测系统的预测能力会随着时间的推移而下降，因为在固定图像数据集上训练的缺陷检测模型无法适应偏差。本文提出了一种人为引导的渐进式学习方法，该方法可系统地向基于视觉的 CNN 检测系统学习新特征。该方法利用直观的用户界面来解决模型漂移问题，通过人类智能来增强表面缺陷检测模型。人类专家在特定条件下监控训练有素的模型性能，从而在实施过程中改变特征，识别错误分类，并启动重新训练。算法会累积分类错误的数据，直到达到预定义的阈值水平或人类专家终止检查。错误分类的结果与原始数据集合并，使用与基础模型开发类似的策略进行逐步再训练。本研究利用预训练的 CNN Efficientnet-b0 通过迁移学习开发锥形滚子检测的表面缺陷检测模型。结论是渐进式再训练提高了缺陷检测性能，减少了错误分类。从混淆矩阵得出的马修斯相关系数（MCC）得分在四次迭代后从 0.6 提高到 0.82。此外，还进行了一项跨模型基准研究，以显示所提方法的多功能性。本研究表明，人类引导的渐进式学习方法可以利用深度学习算法为基于视觉的表面缺陷检测提供适应性，并在实时实施过程中提高系统性能。

{"title":"Augmenting human-guided progressive learning with machine vision systems for robust surface defect detection","authors":"Swarit Anand Singh, Sahil J Choudhari, K.A. Desai","doi":"10.1016/j.aei.2024.102906","DOIUrl":"10.1016/j.aei.2024.102906","url":null,"abstract":"<div><div>Machine vision systems commonly utilize Convolutional Neural Networks (CNNs) for in-line surface defect detection of manufactured components. The prediction abilities of vision-based inspection systems deteriorate with time as the defect detection model trained on fixed image datasets fails to accommodate deviations. This paper proposes a human-guided progressive learning approach that systematically imparts learning of new features to the CNN-powered vision-based defect detection system. The approach augments the surface defect detection model with human intelligence, using an intuitive user interface to address model drift. The human expert monitors the trained model performance under specific conditions leading to the change of characteristics during implementation, identifies misclassifications, and initiates re-training. The algorithm accumulates misclassified data till a pre-defined threshold level is reached or a human expert terminates inspection. The misclassified results merge with the original datasets for progressive re-training using a strategy similar to the base model development. The present work utilizes pre-trained CNN Efficientnet-b0 to develop the surface defect detection model for tapered roller inspection through transfer learning. It is concluded that the progressive re-training improves defect detection performance and reduces misclassifications. The Matthews Correlation Coefficient (MCC) score, derived from the confusion matrix, showed improvement from 0.6 to 0.82 after four iterations. A cross-model benchmarking study is also performed to show the versatility of the proposed approach. The present work demonstrated that the human-guided progressive learning approach can provide adaptability to vision-based surface defect detection utilizing deep learning algorithms and enhance system performance during real-time implementation.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102906"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532007","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}

引用次数: 0

From technology opportunities to solutions generation via patent analysis: Application of machine learning-based link prediction 通过专利分析从技术机遇到解决方案的生成：基于机器学习的链接预测的应用

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102944

Ziliang Wang , Wei Guo , Hongyu Shao , Lei Wang , Zhixing Chang , Yuanrong Zhang , Zhenghong Liu

Technology convergence represents a significant mode of technological innovation that is widely prevalent across various industries. This innovative approach integrates multiple technologies to develop new integrated solutions, thereby fostering a competitive advantage for enterprises. Anticipating future potential technology convergence is of paramount importance for businesses. However, previous research has predominantly relied on the topological information of convergence networks, overlooking the nodal attributes and inter-nodal relationships that have an impact on the emergence of technology convergence. To enhance existing studies, this paper employs three types of features: node attributes and inter-node relationships based on the drivers of technology convergence, along with link prediction similarity indices. Additionally, we utilize Graph Convolutional Neural Network (GCN) for node embedding to leverage node attributes. Machine learning models are utilized for link prediction based on these features to identify potential technology opportunities. To guide research and development (R&D) efforts, we recommend high-value patents for each node using entropy weighting across five metrics that objectively quantify patent value, and transform patent abstracts into vectors using Doc2Vec. Patents with high similarity in abstract text between nodes are utilized to extract technical solutions and fuse ideas for technology convergence. A case study is conducted within the autonomous driving industry, leveraging comprehensive information including node attributes, inter-node relationships, and topology-based similarities to identify technology opportunities and guide the generation of R&D ideas through the convergence of technical solutions.

技术融合是一种重要的技术创新模式，在各行各业广泛流行。这种创新方法将多种技术整合在一起，开发出新的综合解决方案，从而为企业带来竞争优势。预测未来潜在的技术融合对企业至关重要。然而，以往的研究主要依赖于融合网络的拓扑信息，忽略了对技术融合的出现产生影响的节点属性和节点间关系。为了加强现有研究，本文采用了三类特征：基于技术融合驱动因素的节点属性和节点间关系，以及链接预测相似性指数。此外，我们还利用图形卷积神经网络（GCN）进行节点嵌入，以充分利用节点属性。根据这些特征，利用机器学习模型进行链接预测，以识别潜在的技术机会。为了指导研究与开发（R&D）工作，我们使用五个客观量化专利价值的指标进行熵加权，为每个节点推荐高价值专利，并使用 Doc2Vec 将专利摘要转换为向量。节点之间摘要文本相似度高的专利可用于提取技术解决方案和融合技术融合的想法。在自动驾驶行业内开展了一项案例研究，利用包括节点属性、节点间关系和基于拓扑的相似性在内的综合信息来识别技术机会，并通过技术解决方案的融合来引导研发创意的产生。

{"title":"From technology opportunities to solutions generation via patent analysis: Application of machine learning-based link prediction","authors":"Ziliang Wang , Wei Guo , Hongyu Shao , Lei Wang , Zhixing Chang , Yuanrong Zhang , Zhenghong Liu","doi":"10.1016/j.aei.2024.102944","DOIUrl":"10.1016/j.aei.2024.102944","url":null,"abstract":"<div><div>Technology convergence represents a significant mode of technological innovation that is widely prevalent across various industries. This innovative approach integrates multiple technologies to develop new integrated solutions, thereby fostering a competitive advantage for enterprises. Anticipating future potential technology convergence is of paramount importance for businesses. However, previous research has predominantly relied on the topological information of convergence networks, overlooking the nodal attributes and inter-nodal relationships that have an impact on the emergence of technology convergence. To enhance existing studies, this paper employs three types of features: node attributes and inter-node relationships based on the drivers of technology convergence, along with link prediction similarity indices. Additionally, we utilize Graph Convolutional Neural Network (GCN) for node embedding to leverage node attributes. Machine learning models are utilized for link prediction based on these features to identify potential technology opportunities. To guide research and development (R&D) efforts, we recommend high-value patents for each node using entropy weighting across five metrics that objectively quantify patent value, and transform patent abstracts into vectors using Doc2Vec. Patents with high similarity in abstract text between nodes are utilized to extract technical solutions and fuse ideas for technology convergence. A case study is conducted within the autonomous driving industry, leveraging comprehensive information including node attributes, inter-node relationships, and topology-based similarities to identify technology opportunities and guide the generation of R&D ideas through the convergence of technical solutions.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102944"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659054","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}

引用次数: 0

Interpretable large-scale belief rule base for complex industrial systems modeling with expert knowledge and limited data 利用专家知识和有限数据为复杂工业系统建模的可解释大规模信念规则库

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102852

Zheng Lian, Zhijie Zhou, Changhua Hu, Zhichao Feng, Pengyun Ning, Zhichao Ming

Complex system modeling technology is a hot topic. Nowadays, many complex industrial systems present three characteristics: multiple input indicators, limited data and interpretability requirements. With good interpretability, belief rule base (BRB) serves as an efficient tool for modeling complex systems. However, as the number of input indicators of industrial systems increases, BRB suffers from the combinatorial explosion problem, which makes it hard to generate large-scale BRB and optimize it while maintaining its interpretability. For this purpose, an interpretable large-scale BRB is proposed for complex systems with limited data, where expert knowledge can be utilized effectively. First, a framework for generating an initial large-scale BRB using expert knowledge and limited data is developed, including the determination of attribute weight, basic belief degree and rule weight. Afterwards, a new parameter optimization model is designed to reduce the burden of parameter optimization and maintain the interpretability of BRB, where the Adaptive Moment Estimation (Adam) algorithm is adopted to further improve the efficiency of large-scale parameter optimization. Finally, a health assessment case of an inertial navigation system (INS) verifies the proposed method.

复杂系统建模技术是一个热门话题。目前，许多复杂的工业系统具有三个特点：输入指标多、数据有限和可解释性要求高。信念规则库（BRB）具有良好的可解释性，是复杂系统建模的有效工具。然而，随着工业系统输入指标数量的增加，信念规则库出现了组合爆炸问题，很难生成大规模的信念规则库，并在保持其可解释性的同时对其进行优化。为此，我们针对数据有限的复杂系统提出了一种可解释的大规模 BRB，可有效利用专家知识。首先，建立了一个利用专家知识和有限数据生成初始大规模 BRB 的框架，包括属性权重、基本信念度和规则权重的确定。然后，设计了一个新的参数优化模型，以减轻参数优化的负担并保持 BRB 的可解释性，其中采用了自适应矩估计（Adam）算法，进一步提高了大规模参数优化的效率。最后，一个惯性导航系统（INS）的健康评估案例验证了所提出的方法。

{"title":"Interpretable large-scale belief rule base for complex industrial systems modeling with expert knowledge and limited data","authors":"Zheng Lian, Zhijie Zhou, Changhua Hu, Zhichao Feng, Pengyun Ning, Zhichao Ming","doi":"10.1016/j.aei.2024.102852","DOIUrl":"10.1016/j.aei.2024.102852","url":null,"abstract":"<div><div>Complex system modeling technology is a hot topic. Nowadays, many complex industrial systems present three characteristics: multiple input indicators, limited data and interpretability requirements. With good interpretability, belief rule base (BRB) serves as an efficient tool for modeling complex systems. However, as the number of input indicators of industrial systems increases, BRB suffers from the combinatorial explosion problem, which makes it hard to generate large-scale BRB and optimize it while maintaining its interpretability. For this purpose, an interpretable large-scale BRB is proposed for complex systems with limited data, where expert knowledge can be utilized effectively. First, a framework for generating an initial large-scale BRB using expert knowledge and limited data is developed, including the determination of attribute weight, basic belief degree and rule weight. Afterwards, a new parameter optimization model is designed to reduce the burden of parameter optimization and maintain the interpretability of BRB, where the Adaptive Moment Estimation (Adam) algorithm is adopted to further improve the efficiency of large-scale parameter optimization. Finally, a health assessment case of an inertial navigation system (INS) verifies the proposed method.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102852"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417210","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}

引用次数: 0

Smeta-LU: A self-supervised meta-learning fault diagnosis method for rotating machinery based on label updating Smeta-LU：基于标签更新的旋转机械自监督元学习故障诊断方法

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102875

Zhiqian Zhao , Yinghou Jiao , Yeyin Xu , Zhaobo Chen , Runchao Zhao

During operation of rotating machinery, collecting high-quality labeled fault samples is difficult, and the corresponding data annotation is time consuming and costly. Therefore, developing novel intelligent diagnostic methods which can extract key information from massive fault data without labeling is of great significance. In this regard, a self-supervised meta-learning fault diagnosis method for rotating machinery based on label updating, called Smeta-LU, is proposed. It eliminates the pre-training phase and generates meta-tasks directly without labeling information during training. A two-branch framework in Smeta-LU is developed using a contrastive learning approach, which involves the application of a dynamic dictionary to construct samples for one branch, represented by an online encoder. The other branch utilizes the parameters of the former to obtain a target encoder through exponential moving average. To dynamically construct diverse meta-tasks during the meta-training process, each sample in the current batch is treated as a query set, while the support set is selected from queues to construct few-shot tasks, thereby generating a larger pool of candidates. The fault diagnosis task is completed by assigning the label matrix with an optimal transport algorithm and identifying the shots closest to each of the prototype centers. Additionally, the iterative properties of the momentum network and dynamic dictionary are implemented for label updating. The outcomes of two validation experiments demonstrate the superiority and scalability of our self-supervised meta-learning approach compared with conventional supervised meta-learning techniques. Better performance in identifying new fine-grained fault categories is also exhibited during our research.

在旋转机械的运行过程中，收集高质量的标注故障样本十分困难，而相应的数据标注则耗时费钱。因此，开发无需标注即可从海量故障数据中提取关键信息的新型智能诊断方法意义重大。为此，本文提出了一种基于标签更新的旋转机械自监督元学习故障诊断方法，即 Smeta-LU。该方法省去了预训练阶段，在训练过程中无需标注信息，直接生成元任务。Smeta-LU 中的双分支框架是利用对比学习方法开发的，其中包括应用动态字典为一个分支（由在线编码器表示）构建样本。另一个分支利用前一个分支的参数，通过指数移动平均法获得目标编码器。在元训练过程中，为了动态构建多样化的元任务，当前批次中的每个样本都被视为查询集，而支持集则从队列中选取，以构建少量任务，从而生成更大的候选任务池。故障诊断任务是通过最优传输算法分配标签矩阵，并识别最接近每个原型中心的镜头来完成的。此外，动量网络和动态字典的迭代特性也用于标签更新。两个验证实验的结果表明，与传统的监督元学习技术相比，我们的自监督元学习方法具有优越性和可扩展性。在我们的研究中，在识别新的细粒度故障类别方面也表现出了更好的性能。

{"title":"Smeta-LU: A self-supervised meta-learning fault diagnosis method for rotating machinery based on label updating","authors":"Zhiqian Zhao , Yinghou Jiao , Yeyin Xu , Zhaobo Chen , Runchao Zhao","doi":"10.1016/j.aei.2024.102875","DOIUrl":"10.1016/j.aei.2024.102875","url":null,"abstract":"<div><div>During operation of rotating machinery, collecting high-quality labeled fault samples is difficult, and the corresponding data annotation is time consuming and costly. Therefore, developing novel intelligent diagnostic methods which can extract key information from massive fault data without labeling is of great significance. In this regard, a self-supervised meta-learning fault diagnosis method for rotating machinery based on label updating, called Smeta-LU, is proposed. It eliminates the pre-training phase and generates meta-tasks directly without labeling information during training. A two-branch framework in Smeta-LU is developed using a contrastive learning approach, which involves the application of a dynamic dictionary to construct samples for one branch, represented by an online encoder. The other branch utilizes the parameters of the former to obtain a target encoder through exponential moving average. To dynamically construct diverse meta-tasks during the meta-training process, each sample in the current batch is treated as a query set, while the support set is selected from queues to construct few-shot tasks, thereby generating a larger pool of candidates. The fault diagnosis task is completed by assigning the label matrix with an optimal transport algorithm and identifying the shots closest to each of the prototype centers. Additionally, the iterative properties of the momentum network and dynamic dictionary are implemented for label updating. The outcomes of two validation experiments demonstrate the superiority and scalability of our self-supervised meta-learning approach compared with conventional supervised meta-learning techniques. Better performance in identifying new fine-grained fault categories is also exhibited during our research.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102875"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529669","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}

引用次数: 0

Vision-based motion prediction for construction workers safety in real-time multi-camera system 基于视觉的运动预测，在实时多摄像头系统中保障建筑工人安全

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102898

Yuntae Jeon , Dai Quoc Tran , Almo Senja Kulinan , Taeheon Kim , Minsoo Park , Seunghee Park

Ensuring worker safety on dynamic construction sites is a significant challenge, especially as it is crucial to immediately identify potential hazards and warn workers. Existing computer vision-based motion prediction methods often overlook the false negative issue caused by the noisy environments of construction sites, and treat tracking and trajectory prediction as disconnected processes. This study introduces MPSORT, a method that suggests trajectory prediction-based tracking with trajectory interpolation for vision-based automated safety monitoring. The proposed method predicts the future movements of construction workers and vehicles using multiple CCTV cameras, and localizes these predictions onto the construction site’s bird’s eye view (BEV) map. This enables to send the real-time warnings to workers in danger, preventing accidents such as collision, fall, and getting stuck. We evaluated the performance of our method in both object tracking and trajectory prediction tasks on dataset from multiple CCTV cameras on construction sites. The object tracking results show an approximate 10% increase in the number of tracked objects, and the trajectory prediction results indicate an ADE of 7.138 and an FDE of 12.493, reflecting improvements of more than 5% and 2% in ADE and FDE, respectively, compared to previous methods. Overall, these findings are significant for minimizing accidents and enhancing safety and efficiency on construction sites.

确保动态建筑工地的工人安全是一项重大挑战，尤其是立即识别潜在危险并向工人发出警告至关重要。现有的基于计算机视觉的运动预测方法往往忽略了建筑工地嘈杂环境所造成的假负问题，并将跟踪和轨迹预测视为互不关联的过程。本研究介绍的 MPSORT 是一种基于轨迹预测的跟踪方法，建议将轨迹插值用于基于视觉的自动安全监控。所提出的方法利用多个 CCTV 摄像机预测建筑工人和车辆的未来移动，并将这些预测定位到建筑工地的鸟瞰图（BEV）上。这样就能向处于危险中的工人发出实时警告，防止发生碰撞、坠落和卡住等事故。我们在建筑工地多个闭路电视摄像机的数据集上评估了我们的方法在物体跟踪和轨迹预测任务中的性能。物体跟踪结果表明，被跟踪物体的数量增加了约 10%；轨迹预测结果表明，ADE 为 7.138，FDE 为 12.493，与以前的方法相比，ADE 和 FDE 分别提高了 5% 和 2%。总体而言，这些研究结果对于最大限度地减少事故、提高建筑工地的安全和效率具有重要意义。

{"title":"Vision-based motion prediction for construction workers safety in real-time multi-camera system","authors":"Yuntae Jeon , Dai Quoc Tran , Almo Senja Kulinan , Taeheon Kim , Minsoo Park , Seunghee Park","doi":"10.1016/j.aei.2024.102898","DOIUrl":"10.1016/j.aei.2024.102898","url":null,"abstract":"<div><div>Ensuring worker safety on dynamic construction sites is a significant challenge, especially as it is crucial to immediately identify potential hazards and warn workers. Existing computer vision-based motion prediction methods often overlook the false negative issue caused by the noisy environments of construction sites, and treat tracking and trajectory prediction as disconnected processes. This study introduces MPSORT, a method that suggests trajectory prediction-based tracking with trajectory interpolation for vision-based automated safety monitoring. The proposed method predicts the future movements of construction workers and vehicles using multiple CCTV cameras, and localizes these predictions onto the construction site’s bird’s eye view (BEV) map. This enables to send the real-time warnings to workers in danger, preventing accidents such as collision, fall, and getting stuck. We evaluated the performance of our method in both object tracking and trajectory prediction tasks on dataset from multiple CCTV cameras on construction sites. The object tracking results show an approximate 10% increase in the number of tracked objects, and the trajectory prediction results indicate an ADE of 7.138 and an FDE of 12.493, reflecting improvements of more than 5% and 2% in ADE and FDE, respectively, compared to previous methods. Overall, these findings are significant for minimizing accidents and enhancing safety and efficiency on construction sites.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102898"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578040","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}

引用次数: 0

Mixed integer programming and multi-objective enhanced differential evolution algorithm for human–robot responsive collaborative disassembly in remanufacturing system 再制造系统中人机响应协同拆卸的混合整数编程和多目标增强差分进化算法

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102895

Zeqiang Zhang , Wei Liang , Dan Ji , Yanqing Zeng , Yu Zhang , Yan Li , Lixia Zhu

The recycling of waste products is essential for resource reuse. However, turning operation direction causes significant fatigue to operators handling end-of-life (EoL) products, consequently degrading the recycling efficiency. Accordingly, this study employs responsive collaboration robots to aid operators in turning the operation direction of disassembled products. To solve the human-robot responsive collaboration disassembly line balancing problem (HRRC-DLBP), a mixed integer programming (MIP) model is constructed, and a decoding mechanism is designed in this study. Additionally, a multi-objective enhanced differential evolution algorithm (MEDE) in which the decoding mechanism is incorporated is devised and applied to solve the HRRC-DLBP. The MEDE algorithm is validated by comparing its solution results with those of the MIP model. Finally, the MEDE is used to optimise the EoL printer case for the HRRC-DLBP and the disassembly line balancing problem in which the operation direction is turned by humans (H-DLBP). The optimisation results show that the recycling of EoL products is more efficient using the HRRC-DLBP than employing the H-DLBP.

废品回收对资源再利用至关重要。然而，转动操作方向会使操作员在处理报废（EoL）产品时明显感到疲劳，从而降低回收效率。因此，本研究采用响应式协作机器人来帮助操作员转动拆解产品的操作方向。为解决人机协作拆卸线平衡问题（HRRC-DLBP），本研究构建了一个混合整数编程（MIP）模型，并设计了一种解码机制。此外，还设计了一种包含解码机制的多目标增强微分进化算法（MEDE），并将其应用于求解 HRRC-DLBP。通过将 MEDE 算法的求解结果与 MIP 模型的求解结果进行比较，对 MEDE 算法进行了验证。最后，MEDE 被用于优化 EoL 打印机情况下的 HRRC-DLBP 和拆卸线平衡问题，其中拆卸线平衡问题中的操作方向是由人工改变的（H-DLBP）。优化结果表明，使用 HRRC-DLBP 比使用 H-DLBP 的 EoL 产品回收效率更高。

{"title":"Mixed integer programming and multi-objective enhanced differential evolution algorithm for human–robot responsive collaborative disassembly in remanufacturing system","authors":"Zeqiang Zhang , Wei Liang , Dan Ji , Yanqing Zeng , Yu Zhang , Yan Li , Lixia Zhu","doi":"10.1016/j.aei.2024.102895","DOIUrl":"10.1016/j.aei.2024.102895","url":null,"abstract":"<div><div>The recycling of waste products is essential for resource reuse. However, turning operation direction causes significant fatigue to operators handling end-of-life (EoL) products, consequently degrading the recycling efficiency. Accordingly, this study employs responsive collaboration robots to aid operators in turning the operation direction of disassembled products. To solve the human-robot responsive collaboration disassembly line balancing problem (HRRC-DLBP), a mixed integer programming (MIP) model is constructed, and a decoding mechanism is designed in this study. Additionally, a multi-objective enhanced differential evolution algorithm (MEDE) in which the decoding mechanism is incorporated is devised and applied to solve the HRRC-DLBP. The MEDE algorithm is validated by comparing its solution results with those of the MIP model. Finally, the MEDE is used to optimise the EoL printer case for the HRRC-DLBP and the disassembly line balancing problem in which the operation direction is turned by humans (H-DLBP). The optimisation results show that the recycling of EoL products is more efficient using the HRRC-DLBP than employing the H-DLBP.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102895"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532181","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}

引用次数: 0

3D UAV path planning in unknown environment: A transfer reinforcement learning method based on low-rank adaption 未知环境中的 3D 无人机路径规划：基于低阶自适应的转移强化学习方法

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102920

Lan Bo , Tiezhu Zhang , Hongxin Zhang , Jichao Hong , Mingjie Liu , Caihong Zhang , Benyou Liu

The increasing number of application scenarios necessitate unmanned aerial vehicles to possess the capability of autonomous obstacle avoidance and navigation in unknown environments, representing a crucial direction for its development. Path planning plays a crucial role in this process. Path planning aims to design efficient and safe navigation paths for UAVs, thereby significantly reducing energy consumption and time spent while improving equipment adaptability to the environment. Firstly, we employ the deep reinforcement learning algorithm to train the agent on randomly changing maps, enabling it to possess both generalization capabilities and active obstacle avoidance skills. Secondly, a novel framework combining transfer reinforcement learning is proposed. It establishes the pre-trained model and utilizes the enhanced low-rank adaptive algorithm to transfer it into formal training, thereby incorporating prior knowledge and improving the efficacy of formal training. Finally, a novel method of sample abundance is proposed to reuse the experience pool stored in the pre-trained model and further increase the generalization capability of the agent, thereby significantly improving its success rate. The proposed algorithm efficiently uses both the pre-trained model and the experience pool. In practical applications, the pre-trained model can be acquired by training on a limited dataset to endow the agent with autonomous obstacle avoidance capabilities. In formal training, numerous random samples are established to simulate unfamiliar environmental terrains. After rapid training, the agent achieves a success rate of 95% in the test set and demonstrates exceptional performance in smoothness and path length.

越来越多的应用场景要求无人飞行器具备在未知环境中自主避障和导航的能力，这是其发展的一个重要方向。在这一过程中，路径规划起着至关重要的作用。路径规划旨在为无人飞行器设计高效、安全的导航路径，从而在提高设备对环境的适应性的同时，大幅降低能耗和时间消耗。首先，我们采用深度强化学习算法在随机变化的地图上训练代理，使其同时具备泛化能力和主动避障技能。其次，我们提出了一种结合转移强化学习的新型框架。它建立了预训练模型，并利用增强的低秩自适应算法将其转移到正式训练中，从而结合先验知识，提高正式训练的效果。最后，提出了一种新颖的样本丰度方法，以重复利用预训练模型中存储的经验池，进一步提高代理的泛化能力，从而显著提高其成功率。所提出的算法有效地利用了预训练模型和经验池。在实际应用中，预训练模型可以通过在有限的数据集上进行训练来获得，从而赋予代理自主避障能力。在正式训练中，要建立大量随机样本来模拟陌生的环境地形。经过快速训练后，代理在测试集中的成功率达到 95%，并在平滑度和路径长度方面表现出卓越的性能。

{"title":"3D UAV path planning in unknown environment: A transfer reinforcement learning method based on low-rank adaption","authors":"Lan Bo , Tiezhu Zhang , Hongxin Zhang , Jichao Hong , Mingjie Liu , Caihong Zhang , Benyou Liu","doi":"10.1016/j.aei.2024.102920","DOIUrl":"10.1016/j.aei.2024.102920","url":null,"abstract":"<div><div>The increasing number of application scenarios necessitate unmanned aerial vehicles to possess the capability of autonomous obstacle avoidance and navigation in unknown environments, representing a crucial direction for its development. Path planning plays a crucial role in this process. Path planning aims to design efficient and safe navigation paths for UAVs, thereby significantly reducing energy consumption and time spent while improving equipment adaptability to the environment. Firstly, we employ the deep reinforcement learning algorithm to train the agent on randomly changing maps, enabling it to possess both generalization capabilities and active obstacle avoidance skills. Secondly, a novel framework combining transfer reinforcement learning is proposed. It establishes the pre-trained model and utilizes the enhanced low-rank adaptive algorithm to transfer it into formal training, thereby incorporating prior knowledge and improving the efficacy of formal training. Finally, a novel method of sample abundance is proposed to reuse the experience pool stored in the pre-trained model and further increase the generalization capability of the agent, thereby significantly improving its success rate. The proposed algorithm efficiently uses both the pre-trained model and the experience pool. In practical applications, the pre-trained model can be acquired by training on a limited dataset to endow the agent with autonomous obstacle avoidance capabilities. In formal training, numerous random samples are established to simulate unfamiliar environmental terrains. After rapid training, the agent achieves a success rate of 95% in the test set and demonstrates exceptional performance in smoothness and path length.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102920"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572916","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}

引用次数: 0

An optimization-based motion planner for dual-arm manipulation of the soft deformable linear objects with nonnegligible gravity 基于优化的运动规划器，用于双臂操纵具有不可忽略重力的软变形线性物体

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102874

Shirui Wu, Jiwen Zhang, Dan Wu

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.

对可变形线性物体（DLO）的双臂操纵是机器人研究中一个既实用又具有挑战性的问题，为包括电缆装配在内的各种工业应用提供了巨大潜力。为了准确模拟 DLO 的机械特性，我们采用了基尔霍夫微分模型，该模型将 DLO 配置参数化为一个 6 维流形。传统上，解决这一规划问题的方法完全依赖于基于采样的方法，由于必须获得每个样本的 DLO 形状，因此计算成本很高。此外，这些方法完全忽略了重力，假设 DLO 足够坚硬。然而，在许多工业场景中，这一假设并不成立，尤其是在处理软 DLO 时，重力的影响不可忽略，导致稳定性和灵敏度较差。在这项工作中，针对双臂软 DLO 的操纵规划，提出了一种基于优化的新范例，以应对与软 DLO 的性质和重力影响相关的挑战。引入了 "稳定距离 "的概念，作为 DLO 稳定程度的一个易于测量的指标。此外，还对 DLO 局部引导的奇异现象进行了深入研究，以找出其原因并提供有效的解决方案。此外，还介绍了在复杂障碍物环境中避免 DLO 局部陷阱的策略。通过模拟和硬件实验，利用两种长度约为 1 米的软 DLO，在各种环境设置下对综合规划器进行了验证。实验结果表明，该算法在不同的装配场景下均表现出良好的性能。

{"title":"An optimization-based motion planner for dual-arm manipulation of the soft deformable linear objects with nonnegligible gravity","authors":"Shirui Wu, Jiwen Zhang, 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}

引用次数: 0