Frequency Domain Decomposition, including Peak Picking technique
-
Updated
Jan 4, 2022 - Python
Frequency Domain Decomposition, including Peak Picking technique
Peak picking technique for basic frequency domain
This repository is about reading and examining the shaker test data
This repository contains Python scripts used to obtain results from the paper "A Latent Variable Approach for Mitigation of Environmental and Operational Variability in Vibration-Based SHM – A Linear Approach," presented at the EWSHM2024.
Master's Thesis project on using machine learning as a part of a SHM assesment on a damaged, post-tensioned bridge.
Este benchmark ontains a set of data and information relating to researches involving Structural Integrity Monitoring (SHM) via Electromechanical Impedance (EMI) and Guided Waves (GW)
Results for Synthetic Data Augmentation: Deep Leaf-Bootstrapping GAN for Image-based Structural Scene and Damage Identification
Defect Detection and Quantification Toolbox
A method for distributed modal parameters identifiction: distributed single-channel based parameters estimation at edge; multi-channel modal parameters integration at remote.
This suite is an ensemble of codes developed to conduct a vibration analysis of a assembled structure.
Homeworks done for SHM Course on PPGEM (FEIS/UNESP).
Repository containing multiple EOV Mitigation Procedures for Structural Health Monitoring.
This repository is about reading and examining the hammer test data
Structural Health Monitoring System data analysis
Open-source framework for infrastructure defect detection with transfer learning. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711021001503
BayesianNN: Implementation of BNNs for virtual monitoring (offshore wind farms) and uncertainty quantification
Structural Health Monitoring is one of the recent area which has not seen significant application of deep-neural network. The major limitation is due to unavailability of huge number of labeled data set.
MAGNOLIA is a data set of a magneto-elastic system composed of a clamped beam interacting with a permanent magnet in a mono-stable configuration with hardening behavior in healthy and damaged states. This experiment was conducted at the SHM Lab from UNESP Ilha Solteira.
Learning based Image Scale Estimation (LISE) for Quantitative Visual Inspection
Add a description, image, and links to the structural-health-monitoring topic page so that developers can more easily learn about it.
To associate your repository with the structural-health-monitoring topic, visit your repo's landing page and select "manage topics."