Processing multi-axial signals by POD for damage detection

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dc.contributor.author Georgiou, IT en
dc.contributor.author Adams, DE en
dc.contributor.author Bajaj, AK en
dc.date.accessioned 2014-03-01T02:51:10Z
dc.date.available 2014-03-01T02:51:10Z
dc.date.issued 2007 en
dc.identifier.issn 21915644 en
dc.identifier.uri http://hdl.handle.net/123456789/35411
dc.relation.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-84861569761&partnerID=40&md5=730f3de925f9412a8a5173911ea382ce en
dc.subject.other Damage location en
dc.subject.other Damage quantification en
dc.subject.other Fixed points en
dc.subject.other Fundamental operations en
dc.subject.other Impact force en
dc.subject.other Impact response en
dc.subject.other Initial conditions en
dc.subject.other Modal properties en
dc.subject.other Multi-field en
dc.subject.other Network of sensors en
dc.subject.other Nonlinear structure en
dc.subject.other Novel applications en
dc.subject.other Proper orthogonal decompositions en
dc.subject.other Spatio-temporal en
dc.subject.other Triaxial accelerometer en
dc.subject.other Turbulence dynamics en
dc.subject.other Wave analysis en
dc.subject.other Damage detection en
dc.subject.other Flow fields en
dc.subject.other Modal analysis en
dc.subject.other Principal component analysis en
dc.subject.other Random processes en
dc.subject.other Signal processing en
dc.subject.other Structural dynamics en
dc.subject.other Time series en
dc.subject.other Data handling en
dc.title Processing multi-axial signals by POD for damage detection en
heal.type conferenceItem en
heal.publicationDate 2007 en
heal.abstract There is a plethora of signal processing transforms ranging from the classical FFT to the very popular wavelet transform used for modal analysis and damage detection in structures. These transforms operate on a signal at a time. But in practice a set of signals is usually available (network of sensors). To detect the modal properties of a structure from a given spatially distributed set of time series, the whole information must be somehow fused. Time and space auto-correlations are fundamental operations that correlate or fuse distributed (spatio-temporal) data: they give rise to the Proper Orthogonal Decomposition (POD) Transform. Originally introduced to study turbulence dynamics in fluids and in general stochastic processes, the POD transform has been advanced into a powerful data processing tool especially for vibration and wave analysis of nonlinear structures with multi-field response. In this work, we present a novel application of the multi-field POD transform for signal processing. In particular, POD is applied to transform a set of time series recorded by a single tri-axial accelerometer at a fixed point as an impact force ranges over a grid of locations. The technique was applied to study the impact response of a canister-like composite structure: The POD transform reveals that the autocorrelation energy is distributed almost exponentially over a small set of POD modes. The distribution of the POD spectrum is robust but the shapes of the POD modes are sensitive to small changes in initial conditions and imperfections. These features seem to be promising indicators for damage location and damage quantification. en
heal.journalName Conference Proceedings of the Society for Experimental Mechanics Series en

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