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

Gardner, Paul A.; Fuentes, Ramón; Dervilis, Nikolaos; Mineo, Carmelo; Pierce, S. Gareth; Cross, Elizabeth J.; Worden, Keith

doi:10.1098/rsta.2019.0581

Cited by 43 publications

(31 citation statements)

References 34 publications

(59 reference statements)

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“…Nowadays, machine learning (ML) gain tremendous attention in almost every domain in the aerospace industry. In aerospace manufacturing, ML-based DDMs are used to support production line robotics and automation [110]; product design [111]; non-destructive inspection [112]; assembly including inspection, processing and verification [113]; and management including supply and logistic chain management [114], manufacturing scheduling [115], strategical decision-making and recommendation [67]. In in-service M&O, ML-based DDMs is leveraged to airside support including anomaly detection [116]; airframe injury and damage diagnosis [117]; dynamic state estimation and reasoning [118], [119] and maintenance scheduling [120].…”

Section: ) Data-driven Modelmentioning

confidence: 99%

Digital Twin in Aerospace Industry: A Gentle Introduction

Aslam

Wileman

et al. 2022

IEEE Access

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Digital twin (DT), primarily a virtual replica of any conceivable physical entity, is a highly transformative technology with profound implications. Whether it be a product development, design optimisation, performance improvement, or predictive maintenance, digital twins are changing the ways work is undertaken in various industries with multifarious business applications. Aerospace industry, including its manufacturing base, is one such keen adopter of digital twins with an unprecedented interest in their bespoke design, development, and implementation across wider operations and critical functions. This, however, comes with some misconceptions about the digital twin technology and lack of understanding with respect to its optimal implementation. For instance, equating digital twin to an intelligent model while ignoring the essential components of data acquisition and visualisation, misleads the creators into building digital shadow or digital models, instead of the actual digital twin. This paper unfolds such intricacies of digital twin technology for the aerospace community in particular and others in general so as to remove the fallacies that affect their effective realisation for safety-critical systems. It comprises a comprehensive survey of digital twins and their constituent elements. Elaborating their characteristic stateof-the-art composition along with corresponding limitations, three dimensions of the future digital twins for aerospace sector, termed as aero-Digital Twins (aero-DTs), are proposed as an outcome of this survey. These include the interactive, standardisation, and cognitive dimensions of digital twins, which if leveraged diligently could help the aero-DT research and development community quadruple the efficiency of existing and future aerospace systems as well as their associated processes.

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Section: ) Data-driven Modelmentioning

confidence: 99%

Digital Twin in Aerospace Industry: A Gentle Introduction

Aslam

Wileman

et al. 2022

IEEE Access

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“…With proliferation of various machine learning (ML) algorithms, the SHM community has prominently used various supervised learning algorithms (Hou andXia 2020, Avci et al 2021). In (Gardner et al 2020), the authors explained the interface between nondestructive evaluation and machine-learning-based SHM for damage detection. In another study, Su et al (2020) presented a critical review of field monitoring of high-rise structures.…”

Section: Introductionmentioning

confidence: 99%

Multiclass Damage Identification in a Full-Scale Bridge Using Optimally Tuned One-Dimensional Convolutional Neural Network

Sony

Gamage

Sadhu

et al. 2022

J. Comput. Civ. Eng.

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In this paper, a novel method is proposed based on a windowed one-dimensional convolutional neural network (1D CNN) for multiclass damage identification using vibration responses of a full-scale bridge. The measured data is first augmented by extracting samples of windows of raw acceleration time-series to alleviate the problem of a limited training dataset. 1D CNN is developed to classify the windowed time-series into multiple damage classes. The damage is quantified using the predicted class probabilities, and the damage is localized if the predicted class is equal to the assigned damage class, exceeding a threshold associated with majority voting. The proposed network is optimally tuned with respect to various hyper-parameters such as window size, random initialization of weights, etc., to achieve the best classification performance using a global 1D CNN model. The proposed method is validated using the Z24 bridge benchmark data for multiclass classification for two different damage scenarios, namely, pier settlement and rupture of tendons, 1Sony, September 22, 2021 under the various extent of the damage. The damage identification is carried out on various bridge components to collectively identify the structural component with a damaged signature. The resultsshow that the proposed windowed 1D CNN method achieves an accuracy of 97%, and performs well with different types of damage.

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“…There are traditional, machine learning methods with their parametric and non-parametric variants and deep learning methods [ 24 ]. In 2020, Gardner et al presents the power of machine learning with methods such as compressive sensing and transfer learning to solve different structural analysis [ 25 ]. In Chandrasekhar et al, a machine learning approach is used to solve SHM in operational wind-turbine blades.…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Classification in a Jacket-Type Wind-Turbine Foundation Using Principal Component Analysis and Extreme Gradient Boosting

Leon-Medina

Anaya

Mariné

et al. 2021

Sensors

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Damage classification is an important topic in the development of structural health monitoring systems. When applied to wind-turbine foundations, it provides information about the state of the structure, helps in maintenance, and prevents catastrophic failures. A data-driven pattern-recognition methodology for structural damage classification was developed in this study. The proposed methodology involves several stages: (1) data acquisition, (2) data arrangement, (3) data normalization through the mean-centered unitary group-scaling method, (4) linear feature extraction, (5) classification using the extreme gradient boosting machine learning classifier, and (6) validation applying a 5-fold cross-validation technique. The linear feature extraction capabilities of principal component analysis are employed; the original data of 58,008 features is reduced to only 21 features. The methodology is validated with an experimental test performed in a small-scale wind-turbine foundation structure that simulates the perturbation effects caused by wind and marine waves by applying an unknown white noise signal excitation to the structure. A vibration-response methodology is selected for collecting accelerometer data from both the healthy structure and the structure subjected to four different damage scenarios. The datasets are satisfactorily classified, with performance measures over 99.9% after using the proposed damage classification methodology.

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

Cited by 43 publications

References 34 publications

Digital Twin in Aerospace Industry: A Gentle Introduction

Digital Twin in Aerospace Industry: A Gentle Introduction

Multiclass Damage Identification in a Full-Scale Bridge Using Optimally Tuned One-Dimensional Convolutional Neural Network

Structural Damage Classification in a Jacket-Type Wind-Turbine Foundation Using Principal Component Analysis and Extreme Gradient Boosting

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