Papers Structural Health Monitoring
“Structural Health Monitoring” 태그가 달린 논문 197편 · 필터 해제
Digital twin for virtual sensing of ferry quays via a Gaussian Process Latent Force Model
Ferry quays experience rapid deterioration due to their exposure to harsh maritime environments and ferry impacts. Vibration-based structural health monitoring offers a valuable approach to assessing structural integrity…
Structural Health MonitoringStructural Vibration Monitoring with Diffractive Optical Processors
Structural Health Monitoring (SHM) is vital for maintaining the safety and longevity of civil infrastructure, yet current solutions remain constrained by cost, power consumption, scalability, and the complexity of data p…
Autonomous NavigationStructural Health MonitoringMechanical in-sensor computing: a programmable meta-sensor for structural damage classification without external electronic power
Structural health monitoring (SHM) involves sensor deployment, data acquisition, and data interpretation, commonly implemented via a tedious wired system. The information processing in current practice majorly depends on…
Binary ClassificationStructural Health MonitoringBridging the Reality Gap in Digital Twins with Context-Aware, Physics-Guided Deep Learning
Digital twins (DTs) enable powerful predictive analytics, but persistent discrepancies between simulations and real systems--known as the reality gap--undermine their reliability. Coined in robotics, the term now applies…
Domain AdaptationStructural Health MonitoringLocalization of Impacts on Thin-Walled Structures by Recurrent Neural Networks: End-to-end Learning from Real-World Data
Today, machine learning is ubiquitous, and structural health monitoring (SHM) is no exception. Specifically, we address the problem of impact localization on shell-like structures, where knowledge of impact locations aid…
Structural Health MonitoringOn-Device Crack Segmentation for Edge Structural Health Monitoring
Crack segmentation can play a critical role in Structural Health Monitoring (SHM) by enabling accurate identification of crack size and location, which allows to monitor structural damages over time. However, deploying d…
Crack SegmentationSegmentationStructural Health MonitoringFault Diagnosis of 3D-Printed Scaled Wind Turbine Blades
This study presents an integrated methodology for fault detection in wind turbine blades using 3D-printed scaled models, finite element simulations, experimental modal analysis, and machine learning techniques. A scaled …
Fault DetectionFault DiagnosisStructural Health MonitoringData-Driven Structural State Estimation via Multi-Fidelity Gaussian Process Models
Guided wave-based techniques have been used extensively in Structural Health Monitoring (SHM). Models using guided waves can provide information from both time and frequency domains to make themselves accurate and robust…
State EstimationStructural Health MonitoringGuided Wave-Based Structural Awareness Under Varying Operating States via Manifold Representations
Guided wave-based structural health monitoring (SHM) remains a powerful strategy for identifying early-stage defects and safeguarding vital aerospace structures. Yet, its practical use is often hindered by the enormous, …
Data CompressionDiagnosticState EstimationStructural Health MonitoringTowards a Universal Vibration Analysis Dataset: A Framework for Transfer Learning in Predictive Maintenance and Structural Health Monitoring
ImageNet has become a reputable resource for transfer learning, allowing the development of efficient ML models with reduced training time and data requirements. However, vibration analysis in predictive maintenance, str…
Fault DiagnosisStructural Health MonitoringTransfer LearningA Geometric-Aware Perspective and Beyond: Hybrid Quantum-Classical Machine Learning Methods
Geometric Machine Learning (GML) has shown that respecting non-Euclidean geometry in data spaces can significantly improve performance over naive Euclidean assumptions. In parallel, Quantum Machine Learning (QML) has eme…
Quantum Machine LearningStructural Health MonitoringAn optimal baseline selection methodology for data-driven damage detection and temperature compensation in acousto-ultrasonics
The global trends in the construction of modern structures require the integration of sensors together with data recording and analysis modules so that their integrity can be continuously monitored for safe-life, economi…
Structural Health MonitoringActive management of battery degradation in wireless sensor network using deep reinforcement learning for group battery replacement
Wireless sensor networks (WSNs) have become a promising solution for structural health monitoring (SHM), especially in hard-to-reach or remote locations. Battery-powered WSNs offer various advantages over wired systems, …
Deep Reinforcement LearningManagementSchedulingStructural Health MonitoringEfficient dynamic modal load reconstruction using physics-informed Gaussian processes based on frequency-sparse Fourier basis functions
Knowledge of the force time history of a structure is essential to assess its behaviour, ensure safety and maintain reliability. However, direct measurement of external forces is often challenging due to sensor limitatio…
Gaussian ProcessesPrognosisStructural Health MonitoringReal-Time Structural Deflection Estimation in Hydraulically Actuated Systems Using 3D Flexible Multibody Simulation and DNNs
The precision, stability, and performance of lightweight high-strength steel structures in heavy machinery is affected by their highly nonlinear dynamics. This, in turn, makes control more difficult, simulation more comp…
Structural Health MonitoringSurvey of Quantization Techniques for On-Device Vision-based Crack Detection
Structural Health Monitoring (SHM) ensures the safety and longevity of infrastructure by enabling timely damage detection. Vision-based crack detection, combined with UAVs, addresses the limitations of traditional sensor…
QuantizationStructural Health MonitoringFlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM
Automatic crack segmentation is a cornerstone technology for intelligent visual perception modules in road safety maintenance and structural integrity systems. Existing deep learning models and ``pre-training + fine-tuni…
Computational EfficiencyCrack SegmentationGeneral KnowledgeSegmentation+2Vision-based autonomous structural damage detection using data-driven methods
This study addresses the urgent need for efficient and accurate damage detection in wind turbine structures, a crucial component of renewable energy infrastructure. Traditional inspection methods, such as manual assessme…
Structural Health MonitoringData-driven Detection and Evaluation of Damages in Concrete Structures: Using Deep Learning and Computer Vision
Structural integrity is vital for maintaining the safety and longevity of concrete infrastructures such as bridges, tunnels, and walls. Traditional methods for detecting damages like cracks and spalls are labor-intensive…
Instance SegmentationSegmentationSemantic SegmentationStructural Health MonitoringAutomatic selection of the best neural architecture for time series forecasting via multi-objective optimization and Pareto optimality conditions
Time series forecasting plays a pivotal role in a wide range of applications, including weather prediction, healthcare, structural health monitoring, predictive maintenance, energy systems, and financial markets. While m…
State Space ModelsStructural Health MonitoringTime SeriesTime Series Forecasting