scholarly journals A Damage Classification Approach for Structural Health Monitoring Using Machine Learning

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Diego Tibaduiza ◽  
Miguel Ángel Torres-Arredondo ◽  
Jaime Vitola ◽  
Maribel Anaya ◽  
Francesc Pozo
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Sandwich Structure ◽  
Damage Identification ◽  
Principal Component ◽  
Piezoelectric Sensor ◽  
Guided Wave ◽  
Damage Classification ◽  
Structural Health

Inspection strategies with guided wave-based approaches give to structural health monitoring (SHM) applications several advantages, among them, the possibility of the use of real data from the structure which enables continuous monitoring and online damage identification. These kinds of inspection strategies are based on the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects. The principal goal for SHM is oriented to the development of efficient methodologies to process these data and provide results associated with the different levels of the damage identification process. As a contribution, this work presents a damage detection and classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network taking advantage of the use of guided waves, hierarchical nonlinear principal component analysis (h-NLPCA), and machine learning. The methodology is evaluated and tested in two structures: (i) a carbon fibre reinforced polymer (CFRP) sandwich structure with some damages on the multilayered composite sandwich structure and (ii) a CFRP composite plate. Damages in the structures were intentionally produced to simulate different damage mechanisms, that is, delamination and cracking of the skin.

scholarly journals Machine learning at the interface of structural health monitoring and non-destructive evaluation

2020 ◽  
Vol 378 (2182) ◽  
pp. 20190581
Author(s):  
P. Gardner ◽  
R. Fuentes ◽  
N. Dervilis ◽  
C. Mineo ◽  
S.G. Pierce ◽  
...  
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Ultrasonic Inspection ◽  
Guided Wave ◽  
Structural Health ◽  
Non Destructive Evaluation ◽  
Ultrasonic Guided Wave ◽  
Detection And Identification ◽  
Non Destructive

While both non-destructive evaluation (NDE) and structural health monitoring (SHM) share the objective of damage detection and identification in structures, they are distinct in many respects. This paper will discuss the differences and commonalities and consider ultrasonic/guided-wave inspection as a technology at the interface of the two methodologies. It will discuss how data-based/machine learning analysis provides a powerful approach to ultrasonic NDE/SHM in terms of the available algorithms, and more generally, how different techniques can accommodate the very substantial quantities of data that are provided by modern monitoring campaigns. Several machine learning methods will be illustrated using case studies of composite structure monitoring and will consider the challenges of high-dimensional feature data available from sensing technologies like autonomous robotic ultrasonic inspection. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.

Long-term guided wave structural health monitoring in an uncontrolled environment through long short-term principal component analysis

2021 ◽  
pp. 147592172110355
Author(s):  
Kang Yang ◽  
Sungwon Kim ◽  
Rongting Yue ◽  
Haotian Yue ◽  
Joel B. Harley
Keyword(s):  
Principal Component Analysis ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Guided Wave ◽  
Mitigation Strategies ◽  
Reconstruction Method ◽  
Short Term ◽  
Structural Health

Environmental effects are a significant challenge in guided wave structural health monitoring systems. These effects distort signals and increase the likelihood of false alarms. Many research papers have studied mitigation strategies for common variations in guided wave datasets reproducible in a lab, such as temperature and stress. There are fewer studies and strategies for detecting damage under more unpredictable outdoor conditions. This article proposes a long short-term principal component analysis reconstruction method to detect synthetic damage under highly variational environments, like precipitation, freeze, and other conditions. The method does not require any temperature or other compensation methods and is tested by approximately seven million guided wave measurements collected over 2 years. Results show that our method achieves an area under curve score of near 0.95 when detecting synthetic damage under highly variable environmental conditions.

Butterworth Filter Application for Structural Health Monitoring

2016 ◽  
Vol 7 (4) ◽  
pp. 15-29 ◽  
Author(s):  
Ahmed Abdelgawad ◽  
Md Anam Mahmud ◽  
Kumar Yelamarthi
Keyword(s):  
Mathematical Model ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Piezoelectric Sensor ◽  
Guided Wave ◽  
Test Bed ◽  
Explicit Finite Element ◽  
Butterworth Filter ◽  
Structural Health ◽  
Finite Element Software

Most of the existing Structural Health Monitoring (SHM) systems are vulnerable to environmental and operational damages. The majority of these systems cannot detect the size and location of the damage. Guided wave techniques are widely used to detect damage in structures due to its sensitivity to different changes in the structure. Finding a mathematical model for such system will help to implement a reliable and efficient low-cost SHM system. In this paper, a mathematical model is proposed to detect the size and location of damages in physical structures using the piezoelectric sensor. The proposed model combines both pitch-catch and pulse-echo techniques and has been verified throughout simulations using ABAQUS/ Explicit finite element software. For empirical verification, data was collected from an experimental set-up using an Aluminum sheets. Since the experimental data contains a lot of noises, a Butterworth filter was used to clean up the signal. The proposed mathematical model along with the Butterworth filter have been validated throughout real test bed.

scholarly journals A model-based approach for statistical assessment of detection and localization performance of guided wave–based imaging techniques

2017 ◽  
Vol 17 (6) ◽  
pp. 1460-1472 ◽  
Author(s):  
Jérémy Moriot ◽  
Nicolas Quaegebeur ◽  
Alain Le Duff ◽  
Patrice Masson
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Damage Identification ◽  
Guided Wave ◽  
Probability Of Detection ◽  
Size Estimation ◽  
Structural Health ◽  
Model Based ◽  
Localization Performance ◽  
Detection And Localization

This article aims at providing a framework for assessing the detection and localization performance of guided wave–based structural health monitoring imaging systems. The assessment exploits a damage identification metric providing a diagnostic of the structure from an image of the scatterers generated by the system, allowing detection, localization, and size estimation of the damage. Statistical probability of detection and probability of localization curves are produced based on values of the damage identification metric for several damage sizes and positions. Instead of relying on arduous measurements on a significant number of structures instrumented in the same way, a model-based approach is considered in this article for estimating probability of detection and probability of localization curves numerically. This approach is first illustrated in a simplistic model, which allows characterizing the robustness of the structural health monitoring system for various levels of noise in test signals. An experimental test case using a more realistic case with an artificial damage is then considered for validating the approach. A good agreement between experimental and numerical values of the damage identification metric and derived probability of detection and probability of localization curves is observed.

scholarly journals Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features

2021 ◽  
Vol 11 (12) ◽  
pp. 5727
Author(s):  
Sifat Muin ◽  
Khalid M. Mosalam
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Degree Of Freedom ◽  
Absolute Velocity ◽  
Support Vector ◽  
Damage State ◽  
Structural Health ◽  
Cumulative Absolute Velocity

Machine learning (ML)-aided structural health monitoring (SHM) can rapidly evaluate the safety and integrity of the aging infrastructure following an earthquake. The conventional damage features used in ML-based SHM methodologies face the curse of dimensionality. This paper introduces low dimensional, namely, cumulative absolute velocity (CAV)-based features, to enable the use of ML for rapid damage assessment. A computer experiment is performed to identify the appropriate features and the ML algorithm using data from a simulated single-degree-of-freedom system. A comparative analysis of five ML models (logistic regression (LR), ordinal logistic regression (OLR), artificial neural networks with 10 and 100 neurons (ANN10 and ANN100), and support vector machines (SVM)) is performed. Two test sets were used where Set-1 originated from the same distribution as the training set and Set-2 came from a different distribution. The results showed that the combination of the CAV and the relative CAV with respect to the linear response, i.e., RCAV, performed the best among the different feature combinations. Among the ML models, OLR showed good generalization capabilities when compared to SVM and ANN models. Subsequently, OLR is successfully applied to assess the damage of two numerical multi-degree of freedom (MDOF) models and an instrumented building with CAV and RCAV as features. For the MDOF models, the damage state was identified with accuracy ranging from 84% to 97% and the damage location was identified with accuracy ranging from 93% to 97.5%. The features and the OLR models successfully captured the damage information for the instrumented structure as well. The proposed methodology is capable of ensuring rapid decision-making and improving community resiliency.

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