Duetothelargescaleandcomplexityofcivilinfrastructures,structuralhealthmonitoringtypicallyrequiresasubstantialnumberofsensors,whichconsequentlygeneratehugevolumesofsensordata.Innovativesensordatacompressiontechniquesarehighlydesiredtofacilitateefficientdatastorageandremoteretrievalofsensordata.ThispaperpresentsavibrationsensordatacompressionalgorithmbasedontheDifferentialPulseCodeModulation(DPCM)methodandtheconsiderationofeffectsofsignaldistortionduetolossydatacompressiononstructuralsystemidentification.TheDPCMsystemconcernedconsistsoftwoprimarycomponents:linearpredictorandquantizer.FortheDPCMsystemconsideredinthisstudy,theLeastSquaremethodisusedtoderivethelinearpredictorcoefficientsandJayantquantizerisusedforscalarquantization.A5-DOFmodelstructureisusedastheprototypestructureinnumericalstudy.NumericalsimulationwascarriedouttostudytheperformanceoftheproposedDPCM-baseddatacompressionalgorithmaswellasitseffectontheaccuracyofstructuralidentificationincludingmodalparametersandsecondorderstructuralparameterssuchasstiffnessanddampingcoefficients.ItisfoundthattheDPCM-basedsensordatacompressionmethodiscapableofreducingtherawsensordatasizetoasignificantextentwhilehavingaminoreffectonthemodalparametersaswellassecondorderstructuralparametersidentifiedfromreconstructedsensordata.
