Dynamics Modeling of Bearing with Defect in Modelica and Application in Direct Transfer Learning from Simulation to Test Bench for Bearing Fault Diagnosis

dc.contributor.authorRuan, Diwang
dc.contributor.authorChen, Yuxiang
dc.contributor.authorGühmann, Clemens
dc.contributor.authorYan, Jianping
dc.contributor.authorLi, Zhirou
dc.date.accessioned2022-04-01T11:19:30Z
dc.date.available2022-04-01T11:19:30Z
dc.date.issued2022-02-17
dc.date.updated2022-03-23T07:44:19Z
dc.description.abstractIn data-driven bearing fault diagnosis, sufficient fault data are fundamental for algorithm training and validation. However, only very few fault measurements can be provided in most industrial applications, bringing the dynamics model to produce bearing response under defects. This paper built a Modelica model for the whole bearing test rig, including the test bearing, driving motor and hydraulic loading system. First, a five degree-of-freedom (5-DoF) model was proposed for the test bearing to identify the normal bearing dynamics. Next, a fault model was applied to characterize the defect position, defect size, defect shape and multiple defects. The virtual bearing test bench was first developed with OpenModelica and then called in Python with OMPython. For validation of the positive effect of the dynamics model in the direct transfer learning for bearing fault diagnosis, the simulation data from the Modelica model and experimental data from the Case Western Reserve University were fed separately or jointly to train a Convolution Neural Network (CNN). Then the well-trained CNN was transferred directly to achieve the fault diagnosis under the test set consisting of experiment data. Additionally, 157 features were extracted from both time-domain and frequency-domain and fed into CNN as input, and then four different validation cases were designed. The results confirmed the positive effect of simulation data in the CNN transfer learning, especially when the simulation data were added as auxiliary to experimental data, and improved CNN classification accuracy. Furthermore, it indicated that the simulation data from the bearing dynamics model could play a part in the actual experimental measurement when the collected data were insufficient.en
dc.description.sponsorshipDFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berlinen
dc.identifier.eissn2079-9292
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/16665
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-15442
dc.language.isoenen
dc.rightsLicensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc530 Physikde
dc.subject.otherbearingen
dc.subject.othertransfer learningen
dc.subject.otherdynamics modelen
dc.subject.otherfault diagnosisen
dc.subject.othermodelicaen
dc.subject.otherConvolutional Neural Networken
dc.titleDynamics Modeling of Bearing with Defect in Modelica and Application in Direct Transfer Learning from Simulation to Test Bench for Bearing Fault Diagnosisen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber622en
dcterms.bibliographicCitation.doi10.3390/electronics11040622en
dcterms.bibliographicCitation.issue4en
dcterms.bibliographicCitation.journaltitleElectronicsen
dcterms.bibliographicCitation.originalpublishernameMDPIen
dcterms.bibliographicCitation.originalpublisherplaceBaselen
dcterms.bibliographicCitation.volume11en
tub.accessrights.dnbfreeen
tub.affiliationFak. 4 Elektrotechnik und Informatik>Inst. Energie- und Automatisierungstechnik>FG Elektronische Mess- und Diagnosetechnikde
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.groupFG Elektronische Mess- und Diagnosetechnikde
tub.affiliation.instituteInst. Energie- und Automatisierungstechnikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen
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