An artificial neural network methodology for damage detection: Demonstration on an operating wind turbine blade

Movsessian, Artur; Cava, David García; Tcherniak, Dmitri

doi:10.1016/j.ymssp.2021.107766

Cited by 69 publications

(29 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the latest advances in neurosciences and high-capability computing devices, machine learning (ML) algorithms based on Artificial Neural Networks (ANNs) have rapidly emerged as a promising tool to solve damage/defects identification and localization problem [8,10]. Various techniques, including mode shapes, natural frequencies, strain history and many others [11][12][13][14][15], have been used for damage identification during the years for different application fields. This was achieved by utilizing different supervised or unsupervised machine learning techniques for damage recognition [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Damage Detection in Flat Panels by Guided Waves Based Artificial Neural Network Trained through Finite Element Method

et al. 2021

View full text Add to dashboard Cite

Artificial Neural Networks (ANNs) have rapidly emerged as a promising tool to solve damage identification and localization problem, according to a Structural Health Monitoring approach. Finite Element (FE) Analysis can be extremely helpful, especially for reducing the laborious experimental campaign costs for the ANN development and training phases. The aim of the present work is to propose a guided wave-based ANN, developed through the use of the Finite Element Method, to determine the position of damages. The paper first addresses the development and assessment of the modeling technique. The FE model accuracy was proven through the comparison of the predicted results with experimental and analytical data. Then, the ANN was developed and trained on an aluminum plate and subsequently verified in a composite plate, as well as under different damage configurations. According to the results herein proposed, the ANN allowed to detect and localize damages with a high level of accuracy in all cases of study.

show abstract

Section: Introductionmentioning

confidence: 99%

Damage Detection in Flat Panels by Guided Waves Based Artificial Neural Network Trained through Finite Element Method

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Using the covariance as a DSF for damage detection has shown promising results in several studies [22][23][24] and is, therefore, adopted in this study. The covariance of the vibration responses is used to measure the phase and amplitude relationship between the responses at different sensor locations, which characterizes the state of the structure.…”

Section: Covariance-based Damage Sensitive Featuresmentioning

confidence: 99%

Interpretable Machine Learning in Damage Detection Using Shapley Additive Explanations

Movsessian

Cava

Tcherniak

2022

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

Self Cite

View full text Add to dashboard Cite

In recent years, Machine Learning (ML) techniques have gained popularity in Structural Health Monitoring (SHM). These have been particularly used for damage detection in a wide range of engineering applications such as wind turbine blades. The outcomes of previous research studies in this area have demonstrated the capabilities of ML for robust damage detection. However, the primary challenge facing ML in SHM is the lack of interpretability of the prediction models hindering the broader implementation of these techniques. For this purpose, this study integrates the novel Shapley Additive exPlanations (SHAP) method into a ML-based damage detection process as a tool for introducing interpretability and, thus, build evidence for reliable decision-making in SHM applications. The SHAP method is based on coalitional game theory and adds global and local interpretability to ML-based models by computing the marginal contribution of each feature. The contribution is used to understand the nature of damage indices (DIs). The applicability of the SHAP method is first demonstrated on a simple lumped mass-spring-damper system with simulated temperature variabilities. Later, the SHAP method has been evaluated on data from an in-operation V27 wind turbine with artificially introduced damage in one of its blades. The results show the relationship between the environmental and operational variabilities (EOVs) and their direct influence on the damage indices. This ultimately helps to understand the difference between false positives caused by EOVs and true positives resulting from damage in the structure.

show abstract

“…The MD is frequently used for multidimensional outlier detection [1], but as stated by Movsessian et al in [8], MD on its own does not account for EOV, impacting accuracy of damage detection. These findings will be tested by using the MD as novelty detection on the OWT SHM data set.…”

Section: (2022) 032076mentioning

confidence: 99%

Autoencoder and Mahalanobis distance for novelty detection in structural health monitoring data of an offshore wind turbine.

Weil

Weijtjens

Devriendt

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Structural Health Monitoring (SHM) has seen an explosion in data gathering in the last few years. This is illustrated in the offshore wind industry through an increase in the amount of placed offshore wind turbines (OWT), a higher rate of SHM instrumented OWTs and an increase in the sampling rate. The growing data gathering has led to the interest of big data techniques in the SHM industry. This paper introduces a new more robust unsupervised novelty detection pipeline combining an autoencoder and the Mahalanobis distance and comparing this combination to both methods separately. This reduces the high dimensional input to a one dimensional novelty index for detecting anomalies in the OWT. Additionally the research considers the challenges that the downtime of non operationally essential sensors poses, a method is introduced to guarantee high model availability without losing the benefit of high fidelity.

show abstract

An artificial neural network methodology for damage detection: Demonstration on an operating wind turbine blade

Cited by 69 publications

References 47 publications

Damage Detection in Flat Panels by Guided Waves Based Artificial Neural Network Trained through Finite Element Method

Damage Detection in Flat Panels by Guided Waves Based Artificial Neural Network Trained through Finite Element Method

Interpretable Machine Learning in Damage Detection Using Shapley Additive Explanations

Autoencoder and Mahalanobis distance for novelty detection in structural health monitoring data of an offshore wind turbine.

Contact Info

Product

Resources

About