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Implementation of a Piezo-diagnostics Approach for Damage Detection Based on PCA in a Linux-Based Embedded Platform

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dc.contributor.authorCamacho, Jhonatan
dc.contributor.authorQuintero, Andrés
dc.contributor.authorRuiz, Magda
dc.contributor.authorVillamizar, Rodolfo
dc.contributor.authorMujica, Luis
dc.date.accessioned2018-11-13T19:21:35Z
dc.date.available2018-11-13T19:21:35Z
dc.date.issued2018-11
dc.description.abstractThe implementation of damage-detection methods for continuously assessing structural integrity entails systems with attractive features such as storage capabilities, memory capacity, computational complexity and time-consuming processing. In this sense, embedded hardware platforms are a promising technology for developing integrated solutions in Structural Health Monitoring. In this paper, design, test, and specifications for a standalone inspection prototype are presented, which take advantage of piezo-diagnostics principle, statistical processing via Principal Component Analysis (PCA) and embedded systems. The equipment corresponds to a piezoelectric active system with the capability to detect defects in structures, by using a PCA-based algorithm embedded in the Odroid-U3 ARM Linux platform. The operation of the equipment consists of applying, at one side of the structure, wide guided waves by means of piezoelectric devices operated in actuation mode and to record the wave response in another side of the structure by using the same kind of piezoelectric devices operated in sensor mode. Based on the nominal response of the guide wave (no damages), represented by means of a PCA statistical model, the system can detect damages between the actuated/sensed points through squared prediction error (Q-statistical index). The system performance was evaluated in a pipe test bench where two kinds of damages were studied: first, a mass is added to the pipe surface, and then leaks are provoked to the pipe structure by means of a drill tool. The experiments were conducted on two lab structures: (i) a meter carbon-steel pipe section and (ii) a pipe loop structure. The wave response was recorded between the instrumented points for two conditions: (i) The pipe in nominal conditions, where several repetitions will be applied to build the nominal statistical model and (ii) when damage is caused to the pipe (mass adding or leak). Damage conditions were graphically recognized through the Q-statistic chart. Thus, the feasibility to implement an automated real-time diagnostic system is demonstrated with minimum processing resources and hardware flexibility.eng
dc.identifier.issn14248220
dc.identifier.urihttp://hdl.handle.net/20.500.12442/2347
dc.language.isoengeng
dc.publisherMDPIeng
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.licenseLicencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionalspa
dc.sourceSensorseng
dc.sourceVol. 18, No.11 (2018)spa
dc.source.urihttps://doi.org/10.3390/s18113730eng
dc.subjectPrincipal component analysiseng
dc.subjectEmbedded systemeng
dc.subjectOnline monitoringeng
dc.subjectStructural health monitoringeng
dc.subjectGuided waveseng
dc.subjectPipeline damage detectioneng
dc.titleImplementation of a Piezo-diagnostics Approach for Damage Detection Based on PCA in a Linux-Based Embedded Platformeng
dc.typearticleeng
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