Supervised learning strategy for classification and regression tasks applied to aeronautical structural health monitoring problems

Miorelli, Roberto; Kulakovskyi, Andrii; Chapuis, Bastien; D’Almeida, Oscar; Mesnil, Olivier

doi:10.1016/j.ultras.2021.106372

Cited by 35 publications

(22 citation statements)

References 32 publications

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“…Several ML methods have been developed in the last few years to solve various SHM and damage detection problems, especially by using neural networks (NN) [ 1 , 2 , 3 , 4 , 5 ]. Even though ML methods are already well established in vibration-based SHM [ 6 ], their use in guided wave-based SHM is currently rising [ 7 , 8 , 9 ]. For instance, Roy et al [ 7 ] described an unsupervised learning approach for structural damage identification under varying temperatures based on an NN.…”

Section: Introductionmentioning

confidence: 99%

“…Their methodology is validated with measurements from coupon samples in a uniaxial testing machine. More recently, Miorelli et al [ 8 ] demonstrated that support vector machines (SVM) trained on numerical data can be used to solve the inverse problem for damage detection and sizing from experimental guided wave (GW) images. They used a circular array of transducers on an isotropic metal plate with through-holes of different sizes modelled at different locations.…”

Section: Introductionmentioning

confidence: 99%

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Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves

Schnur

Goodarzi

Lugovtsova

et al. 2022

Sensors

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Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves

Schnur

Goodarzi

Lugovtsova

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…A broader and more comprehensive discussion can be found in [97,98], which are two fundamental texts for all people working on SHM. Moreover, supervised learning strategy for classification and regression tasks applied to aeronautical SHM problems was discussed in detail by Miorelli et al [99].…”

Section: Structural Health Monitoringmentioning

confidence: 99%

A Review of Piezoelectric Material-Based Structural Control and Health Monitoring Techniques for Engineering Structures: Challenges and Opportunities

Aabid

Parveez

Raheman³

et al. 2021

Actuators

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With the breadth of applications and analysis performed over the last few decades, it would not be an exaggeration to call piezoelectric materials “the top of the crop” of smart materials. Piezoelectric materials have emerged as the most researched materials for practical applications among the numerous smart materials. They owe it to a few main reasons, including low cost, high bandwidth of service, availability in a variety of formats, and ease of handling and execution. Several authors have used piezoelectric materials as sensors and actuators to effectively control structural vibrations, noise, and active control, as well as for structural health monitoring, over the last three decades. These studies cover a wide range of engineering disciplines, from vast space systems to aerospace, automotive, civil, and biomedical engineering. Therefore, in this review, a study has been reported on piezoelectric materials and their advantages in engineering fields with fundamental modeling and applications. Next, the new approaches and hypotheses suggested by different scholars are also explored for control/repair methods and the structural health monitoring of engineering structures. Lastly, the challenges and opportunities has been discussed based on the exhaustive literature studies for future work. As a result, this review can serve as a guideline for the researchers who want to use piezoelectric materials for engineering structures.

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“…-Decision tree (DT): DT is a supervised learning method. DT constructs the learning model using a set of IF-THEN rules obtained from the training set to predict the output class [88,89]. The hierarchical tree is created based on features in the dataset.…”

mentioning

confidence: 99%

“…-K-nearest neighbor (KNN): It is the simplest supervised learning method. It is known as a lazy learning scheme [87,88]. In this method, we determine the class of the new sample as follows: first, we compare this sample with the training dataset to determine the k closest samples in the training set.…”

mentioning

confidence: 99%

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Rahmani

Yousefpoor

et al. 2021

Mathematics

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Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic and simulate human intelligence, for example, a person’s behavior in solving problems or his ability for learning. Furthermore, ML is a subset of artificial intelligence. It extracts patterns from raw data automatically. The purpose of this paper is to help researchers gain a proper understanding of machine learning and its applications in healthcare. In this paper, we first present a classification of machine learning-based schemes in healthcare. According to our proposed taxonomy, machine learning-based schemes in healthcare are categorized based on data pre-processing methods (data cleaning methods, data reduction methods), learning methods (unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning), evaluation methods (simulation-based evaluation and practical implementation-based evaluation in real environment) and applications (diagnosis, treatment). According to our proposed classification, we review some studies presented in machine learning applications for healthcare. We believe that this review paper helps researchers to familiarize themselves with the newest research on ML applications in medicine, recognize their challenges and limitations in this area, and identify future research directions.

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Supervised learning strategy for classification and regression tasks applied to aeronautical structural health monitoring problems

Cited by 35 publications

References 32 publications

Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves

Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves

A Review of Piezoelectric Material-Based Structural Control and Health Monitoring Techniques for Engineering Structures: Challenges and Opportunities

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

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