A fault detection approach based on one-sided domain adaptation and generative adversarial networks for railway door systems

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dc.contributor.authorShimizu, Minoru
dc.contributor.authorZhao, Yifan
dc.contributor.authorAvdelidis, Nicolas P.
dc.date.accessioned2024-01-05T16:42:37Z
dc.date.available2024-01-05T16:42:37Z
dc.date.issued2023-12-07
dc.description.abstractFault detection using the domain adaptation technique is one of the more promising methods of solving the domain shift problem, and has therefore been intensively investigated in recent years. However, the domain adaptation method still has elements of impracticality: firstly, domain-specific decision boundaries are not taken into consideration, which often results in poor performance near the class boundary; and secondly, information on the source domain needs to be exploited with priority over information on the target domain, as the source domain can provide a rich dataset. Thus, the real-world implementations of this approach are still scarce. In order to address these issues, a novel fault detection approach based on one-sided domain adaptation for real-world railway door systems is proposed. An anomaly detector created using label-rich source domain data is used to generate distinctive source latent features, and the target domain features are then aligned toward the source latent features in a one-sided way. The performance and sensitivity analyses show that the proposed method is more accurate than alternative methods, with an F1 score of 97.9%, and is the most robust against variation in the input features. The proposed method also bridges the gap between theoretical domain adaptation research and tangible industrial applications. Furthermore, the proposed approach can be applied to conventional railway components and various electro-mechanical actuators. This is because the motor current signals used in this study are primarily obtained from the controller or motor drive, which eliminates the need for extra sensors.en_UK
dc.identifier.citationShimizu M, Zhao Y, Avdelidis NP. (2023) A fault detection approach based on one-sided domain adaptation and generative adversarial networks for railway door systems. Sensors, Volume 23, Issue 24, December 2023, Article number 9688en_UK
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s23249688
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20617
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectdata-driven approachen_UK
dc.subjectdeep learningen_UK
dc.subjectdomain adaptationen_UK
dc.subjectdoor systemsen_UK
dc.subjectfault detectionen_UK
dc.subjectgenerative adversarial networken_UK
dc.subjectmachine learningen_UK
dc.subjectrailwayen_UK
dc.titleA fault detection approach based on one-sided domain adaptation and generative adversarial networks for railway door systemsen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2023-12-05

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