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Feature extraction based on independent component analysis of complex neural network fault diagnosis

USA20LuisBAlmeida. ICAoflinearandnonlinearmixtureasedonmutualin {ormation. ISTandINESC a 1D, Lis-bon,[link widoczny dla zalogowanych], PortugalMulti-NeuralNetworksforFaultsDiagnosisBasedonICAFeatureExtractionYangShixiJiaoWeidongWuZhaotong (DepartmentofMechanicalEngineering. ZhejiangUniversity, Hangzhou,[link widoczny dla zalogowanych], 310027, China) Abstract: Artificialneuralnetwork (ANN). especiallytheself-organizingmap (SOM) basedonunsupervisedlearningisakindofexcellentmethodforpatternsclusteringandrecognition. Independentcomponentanslysis (ICA) isapowe, rfultoolforanalyzingnongaussiandata. InICA. theFASTICAbasedonfixed-pointiterationisakindofANNalgorithmwithhigheffi-ciency. whichisspeciallyappropriatetofeatureextractionofmultivariatedataatrealtime. Inthispaper, theFASTICAisfirstlyproposedforfeatureextractionofdifferentmechanicalpatterns (includingnormal, gearfaultandloosefoundation) · followedbycertaintypicalANN (forexampleRBFNorSOM),[link widoczny dla zalogowanych], whichimplementsthefinalclassification. BymeansofICAandthefurtherfeatureextractionstrategybasedonresidualmutualinformation (RMI),[link widoczny dla zalogowanych], higherthansecondorderfeaturesembeddedinmulti-channelvibrationmeasurementscanbecapturedeffectively. Thus, mechanicalfaultpatternscanberecog-nizedcorrectly. TheresultsfromcontrastexperimentsshowedthatthecompoundICA-SOMclassifiercanbeconstructedinsimplerway,[link widoczny dla zalogowanych], andclassifyvariousfaultpatternsatconsiderableaccuracy, bothofwhichimplyitsgreatpotentialinhealthcon-ditionmonitoringofmachines. Keywords: faultdiagnosis; neuralnetwork; independentcomponentanalysis first author : Yang Shixi , male, associate professor , PhD . January 1968 students. Tel : (0571) 87951924; E-mail: yangsx @ zju. edu. cn

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