A Computationally Efficient Bayesian Framework for Structural Health Monitoring using Physics-Based Models

2015 ◽  
Author(s):  
C. Papadimitriou ◽  
C. Argyris ◽  
P. Panetsos
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Bayesian Framework ◽  
Computationally Efficient ◽  
Structural Health

Enhancing Vibration-Based Structural Health Monitoring via Edge Computing: A Tiny Machine Learning Perspective

2021 ◽  
Author(s):  
Federica Zonzini ◽  
Francesca Romano ◽  
Antonio Carbone ◽  
Matteo Zauli ◽  
Luca De Marchi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Network Architecture ◽  
Binary Classification ◽  
Low Cost ◽  
Classification Problems ◽  
Computationally Efficient ◽  
Structural Health

Abstract Despite the outstanding improvements achieved by artificial intelligence in the Structural Health Monitoring (SHM) field, some challenges need to be coped with. Among them, the necessity to reduce the complexity of the models and the data-to-user latency time which are still affecting state-of-the-art solutions. This is due to the continuous forwarding of a huge amount of data to centralized servers, where the inference process is usually executed in a bulky manner. Conversely, the emerging field of Tiny Machine Learning (TinyML), promoted by the recent advancements by the electronic and information engineering community, made sensor-near data inference a tangible, low-cost and computationally efficient alternative. In line with this observation, this work explored the embodiment of the One Class Classifier Neural Network, i.e., a neural network architecture solving binary classification problems for vibration-based SHM scenarios, into a resource-constrained device. To this end, OCCNN has been ported on the Arduino Nano 33 BLE Sense platform and validated with experimental data from the Z24 bridge use case, reaching an average accuracy and precision of 95% and 94%, respectively.

scholarly journals Modelling of Guided Wave Propagation with Spectral Element: Application in Structural Engineering

2014 ◽  
Vol 553 ◽  
pp. 687-692 ◽  
Author(s):  
Ying Wang ◽  
Hong Hao
Keyword(s):  
Wave Propagation ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Engineering ◽  
Spectral Element ◽  
Guided Wave ◽  
Computational Techniques ◽  
Computationally Efficient ◽  
Structural Health ◽  
Guided Wave Propagation

Among many structural health monitoring (SHM) methods, guided wave (GW) based method has been found as an effective and efficient way to detect incipient damages. In comparison with other widely used SHM methods, it can propagate in a relatively long range and be sensitive to small damages. Proper use of this technique requires good knowledge of the effects of damage on the wave characteristics. This needs accurate and computationally efficient modeling of guide wave propagation in structures. A number of different numerical computational techniques have been developed for the analysis of wave propagation in a structure. Among them, Spectral Element Method (SEM) has been proposed as an efficient simulation technique. This paper will focus on the application of GW method and SEM in structural health monitoring. The GW experiments on several typical structures will be introduced first. Then, the modeling techniques by using SEM are discussed.

Structural Health Monitoring via Connectivity Constrained Reference Basis Model Updating

2013 ◽  
Author(s):  
Yoram Halevi
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Model Updating ◽  
Parametric Methods ◽  
Computationally Efficient ◽  
Monitoring Method ◽  
Structural Health ◽  
Sensitivity Functions ◽  
Propagation Analysis ◽  
Monitoring Application

The paper presents a structural health monitoring method that is based on the model updating method connectivity constrained reference basis. The method combines two separate approaches, reference basis and parametric methods, and it is computationally efficient because it does not require calculation of sensitivity functions. The paper discusses why connectivity constrained reference basis is generally suitable for structural health monitoring and what are the modifications required in the new application. The derivation includes noise propagation analysis of the algorithm and its effect on the new structural health monitoring application.

Structural Health Monitoring Algorithm for Composite Plates using Lamb Wave Response Measured by Piezo Transducers

2019 ◽  
Author(s):  
Luis Eduardo Jaramillo Bustamante ◽  
Edison jair Bejarano sepulveda ◽  
Volnei Tita ◽  
Marcelo Leite Ribeiro
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Lamb Wave ◽  
Composite Plates ◽  
Structural Health ◽  
Wave Response ◽  
Monitoring Algorithm
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