6533b873fe1ef96bd12d4470

RESEARCH PRODUCT

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

Elizabeth J. CrossNikolaos DervilisPaul GardnerCarmelo MineoR. FuentesKeith WordenStephen Pierce

subject

Damage detectionComputer scienceTKGeneral MathematicsInterface (computing)General Physics and AstronomyCompressive sensing machine learning non-destructive evaluation structural health monitoring transfer learning ultrasoundMachine learningcomputer.software_genreMachine LearningSettore ING-IND/14 - Progettazione Meccanica E Costruzione Di MacchineEngineeringManufacturing and Industrial FacilitiesNon destructiveHumansUltrasonicsFeature databusiness.industryUltrasonic testingGeneral EngineeringBayes TheoremSignal Processing Computer-AssistedArticlesRoboticsData CompressionIdentification (information)Regression AnalysisStructural health monitoringArtificial intelligenceTransfer of learningbusinesscomputerAlgorithms

description

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’.

yearjournalcountryeditionlanguage
2020-10-16Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
https://doi.org/10.1098/rsta.2019.0581