Frequency response based damage detection using principal component analysis
ABSTRACT In this paper we explore structural damage detection using frequency response signals and principal component analysis. While frequency responses are easy to measure especially in online damage detection applications, most of the associated detection methods are deterministic in nature and cannot deal with uncertainties and noise which are inevitable under practical situations. To tackle this issue and to develop a robust damage detection protocol, here we develop a feature extraction/de-noising methodology based on principal component analysis (PCA). The basic idea is to first establish a feature space of the intact structure response by using multiple measurements. Abnormal signature that is different from the baseline signature can then be identified and magnified after signal reconstruction using the intact structure features. Essentially, the directionality between an inspected signal and the baseline signal in the feature space is used as index of damage occurrence. A series of numerical analyses are performed to characterize the detection system sensitivity and robustness.
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ABSTRACT: This paperpresentsastructuralhealthmonitoring(SHM)techniquethatutilisespattern changesinfrequencyresponsefunctions(FRFs)asinputparametersforasystemof artificialneuralnetworks(ANNs)toassessthestructuralconditionofastructure.To verifytheproposedmethod,itisappliedtonumericalandexperimentalmodelsofatwo- storey framedstructure,onwhichstructuraldamageisinducedbymemberconnectivity and masschanges,respectively.Forthenumericalstructure,simulatedtime-historydata are pollutedwithvariouslevelsofwhiteGaussiannoiseinordertorealisticallyrepresent field-testingconditions.Asadamageindicator,residualFRFsareused,whicharederived by calculatingthedifferencesinFRFdatabetweentheundamaged/baselinestructureand the structurewithchangedjointconditionsoraddedmass.Toobtainsuitablepatterns for neuralnetworktraining,principalcomponentanalysis(PCA)techniquesareadopted to reducethesizeoftheresidualFRFdataandtofilternoise.Ahierarchicalsystemof individualANNs,termednetworkensemble,isthentrainedtomapchangesinPCA- reducedresidualFRFstodamageconditions.Theresultsobtainedforbothdamage investigations,namelyjointdamageandmasschanges,demonstratethattheproposed SHM techniqueisaccurateandreliableinassessingtheconditionoftheteststructure numericallyandexperimentallybasedondirectFRFmeasurementsandnetwork ensembleanalysis.Fromtheoutcomesoftheindividualnetworks,itisfoundthatthe proposedhierarchicalnetworkensembleapproachishighlyefficientinfilteringpoor results ofunderperformingnetworksobtainedfrommeasurementlocationswithlow damagesensitivity.Journal of Sound and Vibration 08/2013; 336(16):3636. · 1.61 Impact Factor
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ABSTRACT: In this paper, an efficient bridge damage detection algorithm is reported. The measured frequency response functions (FRF) is used as the input to artificial neural networks (ANN). Since full size of FRF data is too much for the ANN, a data reduction technique based on principal component analysis (PCA) is applied to extract the features. The extracted features are used as the input data of ANN instead of the raw FRF data. The self-organizing map neural network is chosen because of its superiority in analyzing high-dimensional data without supervising. A steel box girder model with multi damage states is presented to demonstrate the effectiveness of the method. The results showed that it is possible to distinguish the states with good accuracy.Fifth International Conference on Natural Computation, ICNC 2009, Tianjian, China, 14-16 August 2009, 6 Volumes; 01/2009
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ABSTRACT: This paper presents a non-destructive, global, vibration-based damage identification method that utilizes damage pattern changes in frequency response functions (FRFs) and artificial neural networks (ANNs) to identify defects. To extract damage features and to obtain suitable input parameters for ANNs, principal component analysis (PCA) techniques are applied. Residual FRFs, which are the differences in the FRF data from the intact and the damaged structure, are compressed to a few principal components and fed to ANNs to estimate the locations and severities of structural damage. A hierarchy of neural network ensembles is created to take advantage of individual information from sensor signals. To simulate field-testing conditions, white Gaussian noise is added to the numerical data and a noise sensitivity study is conducted to investigate the robustness of the developed damage detection technique to noise. Both numerical and experimental results of simply supported steel beam structures have been used to demonstrate effectiveness and reliability of the proposed method. Copyright © 2009 John Wiley & Sons, Ltd.Structural Control and Health Monitoring 03/2011; · 1.54 Impact Factor