Robustness enhancement of machine fault diagnostic models for railway applications through data augmentation

dc.contributor.authorShi, Dachuan
dc.contributor.authorYe, Yunguang
dc.contributor.authorGillwald, Marco
dc.contributor.authorHecht, Markus
dc.date.accessioned2023-02-22T14:07:23Z
dc.date.available2023-02-22T14:07:23Z
dc.date.issued2022-07-23
dc.description.abstractThe performance of machine learning based machine fault diagnosis (MFD) models could be impaired due to operating condition variations encountered in the real-world industrial environment, such as variations of operating speeds and loads. One major reason for this robustness problem is a lack of adequate training data, especially faulty data, measured in various operating conditions. To cover this gap, we propose a novel data augmentation framework for robustness enhancement in railway MFD applications. First, multibody dynamic simulation (MBS) for physical modeling is applied to simulate arbitrary faulty and operating conditions. Second, fast weighted feature-space averaging (FWFSA) as a new data augmentation technique is developed to augment the simulated faulty data, producing infinite reality-augmented simulation data. The proposed MBS-FWFSA can fit in arbitrary MFD algorithms and transfer-learning settings with minimal effort. Moreover, an in-depth empirical study has been carried out to investigate the causality between condition variations and robustness. A new metric has been defined to evaluate robustness. The experiments also revealed the effect of the proposed MBS-FWFSA and its outperformance against several state-of-the-art augmentation methods. The code and data used in this paper have been shared in our GitHub repository: https://github.com/quickhdsdc/Robustness-Enhancement-of-Machine-Fault-Diagnostic-Models.en
dc.description.sponsorshipEC/H2020/826250/EU/Assets4Rail Measuring, monitoring and data handling for railway assets; bridges, tunnels, tracks and safety systems/Assets4Rail
dc.identifier.eissn1096-1216
dc.identifier.issn0888-3270
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/18233
dc.identifier.urihttps://doi.org/10.14279/depositonce-17026
dc.language.isoen
dc.relation.ispartof10.14279/depositonce-16802
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subject.ddc600 Technik, Medizin, angewandte Wissenschaften::620 Ingenieurwissenschaften::625 Eisenbahn- und Straßenbau
dc.subject.otherdata augmentationen
dc.subject.othermachine fault diagnosisen
dc.subject.othermachine learningen
dc.subject.otherrobustnessen
dc.subject.othercovariate shiften
dc.titleRobustness enhancement of machine fault diagnostic models for railway applications through data augmentation
dc.typeArticle
dc.type.versionacceptedVersion
dcterms.bibliographicCitation.articlenumber108217
dcterms.bibliographicCitation.doi10.1016/j.ymssp.2021.108217
dcterms.bibliographicCitation.journaltitleMechanical Systems and Signal Processing
dcterms.bibliographicCitation.originalpublishernameElsevier
dcterms.bibliographicCitation.originalpublisherplaceAmsterdam
dcterms.bibliographicCitation.volume164
dcterms.rightsHolder.reference§ 38 (4) UrhG
tub.accessrights.dnbfree
tub.affiliationFak. 5 Verkehrs- und Maschinensysteme::Inst. Land- und Seeverkehr (ILS)::FG Schienenfahrzeuge
tub.publisher.universityorinstitutionTechnische Universität Berlin

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