检索结果-内蒙古大学图书馆

40th Chinese Control Conference (CCC)

作者： Qiu, Wen Bao, Cheng Bao, Qiao Hu, Weiwei Wang, Qiang Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China

ISBN: (纸本)9789881563804

In order to solve the problem that the wide range of damage monitoring for large-scale structures, a triangular sparse array-based damage monitoring is researched and validated in this paper. The typical Lamb wave structural responses is analyzed firstly. Multi characteristic parameters is extracted from the responses based on the varies of the response due to the damage. Using these multi characteristic parameters, the damage location in large-scale structure can be monitored by the damage identification model which is designed by using SVM (Support Vector Machine). Experimental validation shown that the multi characteristic parameters from triangular sparse array layout can be used to identify the specific orientation of damage in relative large-scale structure, and the trained SVM based damage identification model can also reveal the damage ***, the existing large-scale structural damage monitoring method is improved.

关键词： Large-scale structural damage monitoring sparse sensor array Lamb wave

来源：评论

学校读者我要写书评

暂无评论

Impact monitoring of large size complex metal structures based on sparse sensor array and transfer learning

引用

ULTRASONICS 2024年 140卷 107305页

作者： Zhao, Bowen Zhang, Yiliang Liu, Qijian Qing, Xinlin Xiamen Univ Sch Aerosp Engn Xiamen 361005 Peoples R China

During aircraft operations, the impact events experienced by the aircraft may cause damage to the structure, thus posing a safety hazard. Therefore, an accurate determination of where the impact occurred and the time history of the impact force can provide an important basis for assessing the condition of the aircraft. However, modern aircraft structures are often large and complex, and relying on dense arrays of sensors for monitoring adds additional weight to the aircraft and reduces the economics of aircraft operation. This paper proposes a region-topoint monitoring strategy. First, a Convolutional Neural Network (CNN) model with region localization capability is trained using the sparse sensor array acquisition data. Then, the weighted center algorithm is used to determine the specific location where the impact occurs, in which the added fuzzy genetic algorithm can provide the ability to adjust weights to improve localization accuracy adaptively. As for the impact force prediction, this paper adopts a model based on a Convolutional Neural Network-Gated Recurrent Unit combined with a SqueezeExcitation attention mechanism (CNN-GRU-SE), which is capable of predicting the impact force occurring in the flat plate and reinforced structure region of the aircraft under different energy conditions. Finally, the impact of incorporating a transfer learning approach on model performance and training cost is investigated for fuselage regions with different structures.

关键词： Impact monitoring Aircraft structure sparse sensor array Machine learning Transfer learning

来源：评论

学校读者我要写书评

暂无评论

sparse sensor array Based Damage Monitoring and Assessment for Large-scale Structures

Sparse Sensor Array Based Damage Monitoring and Assessment f...

引用

第40届中国控制会议

作者： Wen Qiu Cheng Bao Qiao Bao Weiwei Hu Qiang Wang College of Automation & College of Artificial Intelligence Nanjing University of Posts and Telecommunications

In order to solve the problem that the wide range of damage monitoring for large-scale structures,a triangular sparse array-based damage monitoring is researched and validated in this *** typical Lamb wave structural responses is analyzed *** characteristic parameters is extracted from the responses based on the varies of the response due to the *** these multi characteristic parameters,the damage location in large-scale structure can be monitored by the damage identification model which is designed by using SVM(Support Vector Machine).Experimental validation shown that the multi characteristic parameters from triangular sparse array layout can be used to identify the specific orientation of damage in relative large-scale structure,and the trained SVM based damage identification model can also reveal the damage ***,the existing large-scale structural damage monitoring method is improved.

关键词： Large-scale structural damage monitoring sparse sensor array Lamb wave

来源：评论

学校读者我要写书评

暂无评论

Quantitative identification of damage in composite structures using sparse sensor arrays and multi-domain-feature fusion of guided waves

引用

MEASUREMENT 2023年第1期208卷

作者： Tang, Lingquan Li, Yehai Bao, Qiao Hu, Weiwei Wang, Qiang Su, Zhongqing Yue, Dong Nanjing Univ Posts & Telecommun Coll Automat & Coll AI Nanjing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Hong Kong Polytech Univ Dept Mech Engn Kowloon Hong Kong Peoples R China

Damage detection techniques using Lamb waves have shown excellent capabilities in the diagnosis of composite structures. However, structural health monitoring of composite structures is challenging, especially for damage classification. This study proposes a machine learning-based method with a sparse sensor array to achieve quantitative classification of the damage location and severity on a composite plate. First, multi features extraction is used to construct a support vector machine (SVM) damage localization model. Second, optimal path extraction combined with principal component analysis (PCA) is used to construct an SVM model for classifi-cation. To reduce the operational burden of structures, the sparse array is employed. To improve the damage classification accuracy, Fisher clustering is proposed to extract the optimal detection path. Then, PCA is used to achieve data fusion. Experimental results on a glass fiber-reinforced epoxy composite laminate plate demonstrate that the proposed technique can accurately locate and classify the quantitative artificial damage.

关键词： Lamb wave Composite structures sparse sensor array Quantitative classification Support vector machine (SVM)

来源：评论

学校读者我要写书评

暂无评论

An improved D-S evidence fusion algorithm for sub-area collaborative guided wave damage monitoring of large-scale structures

引用

ULTRASONICS 2025年 152卷 107644页

作者： Du, Yuelin Ning, Hongmei Li, Yehai Bao, Qiao Wang, Qiang Nanjing Univ Posts & Telecommun Coll Automat Nanjing Peoples R China Nanjing Univ Posts & Telecommun Coll AI Nanjing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

Due to the development of new materials and advanced manufacturing technologies, the application of largescale composite structures has become increasingly widespread. Ensuring the safe and stable operation of such structures presents new challenges across various application domains. Addressing the limitations of existing guided wave structural health monitoring methods for online damage monitoring in large-scale structures, such as cumbersome equipment setup, insufficient signals coverage, and difficulties in processing massive data, a method for sub-area collaborative guided-wave-based structural damage monitoring and severity assessment based on sparse sensing is proposed. By employing a sparse sensing array layout, the structure is divided into multiple monitoring sub-areas with arranged sensing arrays to reduce overall complexity. The characteristic responses of the guided wave signals from different sub-areas are extracted to construct feature sub-spaces. Support vector machines are adopted to construct evaluation sub-networks in each feature sub-space, enabling regional monitoring. Additionally, an improved D-S evidence fusion algorithm is applied to fuse the decision-layer inputs from each evaluation sub-network, effectively utilizing the feature information from multiple sub-areas and enhancing the accuracy of damage severity assessment for large-scale structures. Experimental results on typical composite structure specimens demonstrate that by fusing the support vector machine evaluation results from each sub-area, the accuracy of damage severity assessment reaches 97.5%, with uncertainties in the severity assessment below 5%.

关键词： Guided wave Composite structures sparse sensor array support vector machine (SVM) D -S evidence fusion algorithm

来源：评论

学校读者我要写书评

暂无评论

Impact monitoring based on domain adversarial transfer learning strategies

引用

SMART MATERIALS AND STRUCTURES 2025年第3期34卷 035017-035017页

作者： Zhao, Bowen Qing, Xinlin Wang, Yishou Liu, Qijian Yan, Jiajia Wang, Yihan Liao, Yunlai Xiamen Univ Sch Aerosp Engn Xiamen 361005 Peoples R China

Impact events may cause some damage to aerospace composite structures that are difficult to inspect on the surface, thus threatening the operational safety of the aircraft. Therefore, estimating the impact location and the original impact force is necessary. This paper proposes a deep-learning model for impact monitoring based on feature extraction. The first step employs a convolutional neural network to localize the impact region, initially narrowing it to a specific area and then determining a precise location using a weighted center algorithm. In the second part, the temporal convolutional network is first utilized for feature extraction, and then the gated recurrent unit is used for impact force estimation. During the training of the impact monitoring model, a domain-adversarial transfer learning strategy is employed to extract domain-invariant features between the source and target domains, reducing the data needed for training. This method can monitor impacts on large, complex composite structures using sparse sensor arrays.

关键词： impact monitoring composite structure sparse sensor array deep learning domain adversarial

来源：评论

学校读者我要写书评

暂无评论

Physical constraint flow field reconstruction method for immersion scan-step flow

引用

FLOW MEASUREMENT AND INSTRUMENTATION 2024年 99卷

作者： Zhan, Binpeng Li, Jialu Liu, Weiting Hu, Liang Fu, Xin Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China

The lithography immersion flow field is highly sensitive to minor variations due to the precision demands of lithography. The scanning flow induces pressure fluctuations on the lens surface, subsequently impacting the quality of exposure. Previous measurement methods have encountered sparse distribution measurements on the lens surface due to limitations in sensor size and flow field dimensions. In this manuscript, the authors propose a modal fusion flow field reconstruction method, effectively integrating sensor-based experimental methods and equation-based computational methods. This method achieves high-resolution measurements of stress distribution and velocity distribution. Initially, a simulation of the flow field under operational conditions is conducted to acquire a dataset, followed by decomposing the dataset into modes to establish a modal library. Throughout the measurement process, the modal library is dynamically restructured based on sensor signal using a sparse representation method to forecast the flow field, and subsequently, the flow field is corrected in accordance with the Navier-Stokes equation.

关键词： Flow field reconstruction sparse sensor array sparse representation Mode order reduction Immersion flow field

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：