Structural Control & Health Monitoring最新文献_第7页

Fatigue Monitoring of 321 Steel Coated by Laser Additively Manufactured CoCrFeMnNi High-Entropy Alloy Using Acoustic Emission Technique 激光增材制备CoCrFeMnNi高熵合金涂层321钢的声发射疲劳监测

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-22 DOI: 10.1155/stc/9115819

Wei Li, Shengnan Hu, Shunpeng Zhu, Cong Li, Guowei Bo, Chipeng Zhang, Dapeng Jiang, Hui Chen, Jianjun He, Wenjun Duan, Jian Chen

{"title":"Fatigue Monitoring of 321 Steel Coated by Laser Additively Manufactured CoCrFeMnNi High-Entropy Alloy Using Acoustic Emission Technique","authors":"Wei Li, Shengnan Hu, Shunpeng Zhu, Cong Li, Guowei Bo, Chipeng Zhang, Dapeng Jiang, Hui Chen, Jianjun He, Wenjun Duan, Jian Chen","doi":"10.1155/stc/9115819","DOIUrl":"https://doi.org/10.1155/stc/9115819","url":null,"abstract":"<div>\u0000 <p>Fatigue failure is a common mode of deterioration for steel cables (e.g., 321 stainless steel) in cable-stayed bridges. In this case, given that the FeCoNiCrMn high-entropy alloy (HEA) coatings have been found to simultaneously improve the fatigue and corrosion resistance of 321 steel, the fatigue crack growth behavior of 321 steel coated with selective laser melting CoCrFeMnNi HEA was further studied in this work. The results indicate that the CoCrFeMnNi alloy coating is able to increase the fatigue crack growth resistance of 321 steel by 21.43% compared to the uncoated 321 steel, and this is because the initiation of crack is mitigated by the angular disparities between adjacent grains and an increased dislocation density in the coating. Furthermore, the acoustic emission (AE) technique was used to track fatigue damage and predict fatigue crack growth life. It was found that crack length could be effectively monitored and predicted using the count and energy parameter, suggesting material and stress ratio independence in the AE technique.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/9115819","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiscenario Generalization Crack Detection Network Based on the Visual Foundation Model 基于可视化基础模型的多场景泛化裂纹检测网络

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-21 DOI: 10.1155/stc/6269747

Shiwei Luo, Xiongyao Xie, Biao Zhou, Kun Zeng, Jun Guo

{"title":"Multiscenario Generalization Crack Detection Network Based on the Visual Foundation Model","authors":"Shiwei Luo, Xiongyao Xie, Biao Zhou, Kun Zeng, Jun Guo","doi":"10.1155/stc/6269747","DOIUrl":"https://doi.org/10.1155/stc/6269747","url":null,"abstract":"<div>\u0000 <p>Recently, convolutional neural networks (CNNs) and hybrid networks, which integrate CNN with Transformer, have been widely employed in structuring crack detection, effectively addressing the challenges of high-precision crack identification in controlled scenes. However, scene generalization remains a significant challenge for existing networks, especially under limited dataset conditions. With the rapid development of foundation models (like ChatGPT), achieving scene generalization has become feasible. In this paper, by taking tunnel crack detection as the background, the CraSAM network is proposed, which incorporates a foundation model-based encoder and a prompt transfer learning module. Based on six datasets including tunnel, bridge, building, and pavement, the CraSAM is compared with 15 state-of-the-art models, including Unet, DeepLabv3+, SSSeg, and TransUNet. It exhibits superior generalization capability both on few-sample learned and unlearned conditions. This work will benefit to investigate of new ways for the utilization of the visual foundation model in various professional fields.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6269747","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

One-Dimensional Deep Convolutional Neural Network-Based Intelligent Fault Diagnosis Method for Bearings Under Unbalanced Health and High-Class Health States 基于一维深度卷积神经网络的轴承不平衡健康和高健康状态智能故障诊断方法

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-17 DOI: 10.1155/stc/6498371

Temesgen Tadesse Feisa, Hailu Shimels Gebremedhen, Fasikaw Kibrete, Dereje Engida Woldemichael

{"title":"One-Dimensional Deep Convolutional Neural Network-Based Intelligent Fault Diagnosis Method for Bearings Under Unbalanced Health and High-Class Health States","authors":"Temesgen Tadesse Feisa, Hailu Shimels Gebremedhen, Fasikaw Kibrete, Dereje Engida Woldemichael","doi":"10.1155/stc/6498371","DOIUrl":"https://doi.org/10.1155/stc/6498371","url":null,"abstract":"<div>\u0000 <p>Modern industrial systems depend heavily on rotating machines, especially rolling element bearings (REBs), to facilitate operations. These components are prone to failure under harsh and variable operating conditions, leading to downtime and financial losses, which emphasizes the need for accurate REB fault diagnosis. Recently, interest has surged in using deep learning, particularly convolutional neural networks (CNNs), for bearing fault diagnosis. However, training CNN models requires extensive data and balanced bearing health states, which existing methods often assume. In addition, while practical scenarios encompass a diverse range of bearing fault conditions, current methods often focus on a limited range of scenarios. Hence, this paper proposes an enhanced method utilizing a one-dimensional deep CNN to ensure reliable operation, with its effectiveness evaluated on Case Western Reserve University (CWRU) rolling bearing datasets. The experimental results showed that the diagnostic accuracy reached 100% under 0∼3 hp working loads for 10 unbalanced health classes. Moreover, it attained 100% accuracy for high-class health states with 20, 30, and 40 classes, and when extended to 64 health classes, it reached a peak accuracy of 99.96%. Thus, the method achieved improved classification ability and stability by employing a straightforward model architecture, along with the integration of batch normalization and dropout operations. Comparative analysis with existing diagnostic methods further underscores the model superiority, particularly in scenarios involving unbalanced and high-class health states, thus emphasizing its effectiveness and robustness. These findings significantly advance the field of intelligent bearing fault diagnosis.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6498371","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid-Driven Digital Twin Framework for Time-Variant Reliability Assessment of Civil Structures 土木结构时变可靠性评估的混合驱动数字孪生框架

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-16 DOI: 10.1155/stc/1167999

Yu Xin, Yu-Sen Cai, Zuo-Cai Wang, Jun Li, Wei-Chao Hou, Chao Li

{"title":"Hybrid-Driven Digital Twin Framework for Time-Variant Reliability Assessment of Civil Structures","authors":"Yu Xin, Yu-Sen Cai, Zuo-Cai Wang, Jun Li, Wei-Chao Hou, Chao Li","doi":"10.1155/stc/1167999","DOIUrl":"https://doi.org/10.1155/stc/1167999","url":null,"abstract":"<div>\u0000 <p>This paper proposes a novel hybrid-driven digital twin (DT) framework for time-variant reliability assessment of civil structures, which mainly consists of four modules, including physics model construction, data-driven model calibration, failure probability calculation, and time-variant reliability prediction. In the first module, a DT model of a specific structure is constructed to simulate structural dynamic responses. Then, an improved unscented Kalman filter (IUKF) algorithm is performed to continuously calibrate the parameters of DT model. Subsequently, in module 3, the subset simulation (SS) approach is employed to calculate failure probability of structures subjected to various model parameter samples, and the generated input–output samples are further applied for metamodel training. A Kriging metamodeling is used to construct the correlation between model parameters and structural failure probability. Once the metamodel is well trained, the time-variant reliability assessment of structures can be continuously achieved in module 4. Numerical simulations on a Bouc–Wen model are conducted to validate the feasibility and accuracy of the proposed approach. Furthermore, a scaled column shake table structure is further employed to verify the effectiveness of the proposed approach. Both numerical and experimental results have shown that the proposed approach is capable of conducting time-variant reliability assessment of civil structures.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1167999","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Unsupervised Structural Damage Diagnosis Method Based on Deep Learning and Sensor Interrelationships 基于深度学习和传感器相互关系的无监督结构损伤诊断方法

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-15 DOI: 10.1155/stc/8821227

Wen-Sheng Zhang, Hong-Nan Li, Xing Fu, Zheng-Li Gu

{"title":"An Unsupervised Structural Damage Diagnosis Method Based on Deep Learning and Sensor Interrelationships","authors":"Wen-Sheng Zhang, Hong-Nan Li, Xing Fu, Zheng-Li Gu","doi":"10.1155/stc/8821227","DOIUrl":"https://doi.org/10.1155/stc/8821227","url":null,"abstract":"<div>\u0000 <p>This paper presents a novel unsupervised method for structural damage diagnosis, which transforms the problem of structural damage diagnosis into the problem of identifying anomalous data in monitoring data. The method establishes the sensor interrelationships based on the graph structure, optimizes the hyperparameters of the graph neural network (GNN) model, and realizes the structural response prediction. By calculating the discrepancy between the predicted response and the monitoring data, the method identifies the anomalies to facilitate the identification and localization of structural damage. The efficiency of the proposed method for bolt loosening detection was evaluated through the analysis of acceleration data collected from a vibrating grandstand simulator and strain data from a wind tunnel test of a scaled tower model. The experimental results indicated that the established connections can provide a preliminary indication of the relative importance of the sensors, which may also be regarded as a metric for each node in the structure. The proposed method is effective in the detection and localization of minor damage in infrastructure structures.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8821227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital Twin-Empowered Analysis of Structural Temperature Field of a Long-Span Suspension Bridge 某大跨度悬索桥结构温度场的数字双赋权分析

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-15 DOI: 10.1155/stc/8021513

Ning-Jie Zhou, You-Lin Xu, Zi-Jing Wei, Di Wu, Er-Hua Zhang

{"title":"Digital Twin-Empowered Analysis of Structural Temperature Field of a Long-Span Suspension Bridge","authors":"Ning-Jie Zhou, You-Lin Xu, Zi-Jing Wei, Di Wu, Er-Hua Zhang","doi":"10.1155/stc/8021513","DOIUrl":"https://doi.org/10.1155/stc/8021513","url":null,"abstract":"<div>\u0000 <p>Structural temperature field significantly affects structural responses, such as displacements and stresses, of a long-span suspension bridge. An accurate and effective analysis of structural temperature field is therefore important. This study proposes a digital twin-empowered analysis of structural temperature field of a long-span suspension bridge. The real bridge and its surrounding environment are regarded as a physical entity. The information, such as ambient temperature and structural temperature, collected by the structural health monitoring system at the locations of sensors is taken as the data collected from the physical entity. A 3D finite element model of the bridge is then constructed as a virtual entity for heat transfer analysis with solar radiation, wind speed, and other environmental conditions included. The data collected from the physical entity are then mapped to the virtual entity through a particle swarm optimization algorithm to update uncertain parameters in the thermal boundary and convert the virtual entity to a digital twin. The established digital twin is finally used to find and predict the structural temperature field of the entire bridge. The results demonstrate that the digital twin-empowered heat transfer analysis is feasible and able to provide more accurate prediction of the structural temperature field of the entire bridge.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8021513","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bridge Girder-End Displacement Reconstruction Using a Novel Hybrid Attention Mechanism Leveraging Multisource Information 利用多源信息的新型混合注意机制重建桥梁梁端位移

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-11 DOI: 10.1155/stc/8249455

Guang Qu, Mingming Song, Ye Xia, Limin Sun

{"title":"Bridge Girder-End Displacement Reconstruction Using a Novel Hybrid Attention Mechanism Leveraging Multisource Information","authors":"Guang Qu, Mingming Song, Ye Xia, Limin Sun","doi":"10.1155/stc/8249455","DOIUrl":"https://doi.org/10.1155/stc/8249455","url":null,"abstract":"<div>\u0000 <p>In the realm of structural health monitoring (SHM) of bridge structures, the accurate reconstruction of girder-end displacement (GED) is crucial for identifying potential structural damage and ensuring the monitoring system’s reliability. A novel fine-grained spatial (FGS) attention mechanism, combined with efficient channel attention (ECA), has been proposed to effectively utilize multisource monitoring data. This hybrid attention mechanism has been integrated into an arithmetic optimization algorithm–bidirectional long short-term memory (AOA–BiLSTM) framework for reconstructing GED using non-GED data, including deflection, temperature, strain, and traffic data. Data are organized into a two-dimensional array based on sensor types and spatial locations to capture interchannel and intrachannel correlations. ECA captures local correlations among different sensor types, while the proposed FGS enhances model interpretability by focusing on local dependencies within each sensor type. Huber loss is employed for robust performance, and AOA techniques are used for efficient hyperparameter optimization. Validation with real-world data from a cable-stayed bridge demonstrates the necessity and efficacy of considering multidimensional information correlations in response reconstruction for SHM applications. This work lays a theoretical foundation for improving safety assessments, anomaly detection, data recovery, and virtual sensing in bridge structures.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8249455","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Numerical Simulations of Shaking Table Tests of Metro-Induced Vertical Vibrations of Interstory-Isolated, Base-Isolated, and Fixed-Base Structures 层间隔震、基础隔震和固定基础结构地铁诱发竖向振动的振动台试验数值模拟

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-10 DOI: 10.1155/stc/8105608

Xinyu Wang, Linlin Xie, Aiqun Li, Tao Wang, Cantian Yang

{"title":"Numerical Simulations of Shaking Table Tests of Metro-Induced Vertical Vibrations of Interstory-Isolated, Base-Isolated, and Fixed-Base Structures","authors":"Xinyu Wang, Linlin Xie, Aiqun Li, Tao Wang, Cantian Yang","doi":"10.1155/stc/8105608","DOIUrl":"https://doi.org/10.1155/stc/8105608","url":null,"abstract":"<div>\u0000 <p>Metro networks have been extensively developed in large cities to satisfy traffic demands. Adjacent to subways, there has been an increasing construction of fixed-base, base-isolated, and interstory-isolated buildings on metro depots. Notably, metro-induced environmental vibrations have led to vibrations in buildings, thus affecting human health and the regular operation of sensitive equipment. Numerical simulations are considered a valuable method for assessing building vibrations. However, research on a generalized numerical simulation strategy for simulating the metro-induced vibrations of the abovementioned three types of buildings remains rare. Hence, this study recommends a generalized numerical simulation strategy and validates it through the comparison between the results of shaking table tests. The acceleration time histories of floor, distributions of the acceleration at different positions on the slab and along the height of the building, and one-third octave band vertical acceleration levels were accurately simulated for the three structures. Meanwhile, the simulation accuracies of three types of damping models were discussed. The relative differences between the simulated and experimental maximum acceleration amplification coefficients and one-third octave band vertical acceleration levels were both less than 4.2%. Furthermore, the influences of the mesh sizes of the elements for the slabs and the parameters of the Rayleigh damping model on the simulated results were investigated. The recommended simulation strategy can contribute to further investigation of the metro-induced vertical vibration assessment of different types of structures.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8105608","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vision-Aided Damage Detection With Convolutional Multihead Self-Attention Neural Network: A Novel Framework for Damage Information Extraction and Fusion 基于卷积多头自注意神经网络的视觉辅助损伤检测：一种新的损伤信息提取与融合框架

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-08 DOI: 10.1155/stc/1677778

Yiming Zhang, Zili Xu, Guang Li, Jun Wang

{"title":"Vision-Aided Damage Detection With Convolutional Multihead Self-Attention Neural Network: A Novel Framework for Damage Information Extraction and Fusion","authors":"Yiming Zhang, Zili Xu, Guang Li, Jun Wang","doi":"10.1155/stc/1677778","DOIUrl":"https://doi.org/10.1155/stc/1677778","url":null,"abstract":"<div>\u0000 <p>The current application of vibration-based damage detection is constrained by the low spatial resolution of signals obtained from contact sensors and an overreliance on hand-engineered damage indices. In this paper, we propose a novel vision-aided framework featuring convolutional multihead self-attention neural network (CMSNN) to deal with damage detection tasks. To meet the requirement of spatially intensive measurements, a computer vision algorithm called optical flow estimation is employed to provide informative enough mode shapes. As a downstream process, a CMSNN model is designed to autonomously learn high-level damage representations from noisy mode shapes without any manual feature design. In contrast to the conventional approach of solely stacking convolutional layers, the model is enhanced by combining a convolutional neural network (CNN)–based multiscale information extraction module with an attention-based information fusion module. During the training process, various scenarios are considered, including measurement noise, data missing, multiple damages, and undamaged samples. Moreover, the parameter transfer strategy is introduced to enhance the universality of the application. The performance of the proposed framework is extensively verified via datasets based on numerical simulations and two laboratory measurements. The results demonstrate that the proposed framework can provide reliable damage detection results even when the input data are corrupted by noise or incomplete.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1677778","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multisource Heterogeneous Information Selective Fusion Network for Fault Diagnosis of Rolling Bearings 多源异构信息选择融合网络用于滚动轴承故障诊断

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-07 DOI: 10.1155/stc/6606543

Shoucong Xiong, Leping Zhang, Yingxin Yang, Hongdi Zhou, Leilei Zhang

{"title":"Multisource Heterogeneous Information Selective Fusion Network for Fault Diagnosis of Rolling Bearings","authors":"Shoucong Xiong, Leping Zhang, Yingxin Yang, Hongdi Zhou, Leilei Zhang","doi":"10.1155/stc/6606543","DOIUrl":"https://doi.org/10.1155/stc/6606543","url":null,"abstract":"<div>\u0000 <p>Till now, deep learning–based intelligent diagnosis models combined with multisource information have become popular, but tough issues like multisource feature extraction and information redundancy may sacrifice the models’ representational power and result in a degraded performance. Aiming at the above problems, this paper proposed a novel model called multisource heterogeneous information selective fusion network (MHI-SFN) for rolling bearing fault diagnosis. In MHI-SFN, multisource heterogeneous signals were stacked together and directly fed into model and grouped convolution was adopted to replace standard convolution throughout the structure, enabling kernels to firstly focus on the feature extraction of every individual signal and then perform efficient feature fusion work as needed. Then, selective kernel modules were designed to adaptively assign suitable kernel sizes and selectively fuse the valuable information between different scales of feature map from different signal sources. Lastly, channel attention was introduced to adaptively alleviate the information correlation and redundancy between the extracted features. Compared with other multisource information–based methods, MHI-SFH automatically solves the multisource feature fusion and information redundancy problems with its specially designed structure, avoiding complicated hand-crafted signal processing steps and achieving a powerful end-to-end intelligent fault diagnosis. The proposed method was experimentally verified on two rolling bearing datasets, and the results proved the feasibility and superiority of the MHI-SFN model.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6606543","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0