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A Methodology for Prognostics Under the Conditions of Limited Failure Data Availability
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Scania Commercial Vehicles, Service Support Solutions, Sweden.
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 183996-184007Article in journal (Refereed) Published
Abstract [en]

When failure data are limited, data-driven prognostics solutions underperform since the number of failure data samples is insufficient for training prognostics models effectively. In order to address this problem, we present a novel methodology for generating failure data which allows training datasets to be augmented so that the number of failure data samples is increased. In contrast to existing data generation techniques which duplicate or randomly generate data, the proposed methodology is capable of generating new and realistic failure data samples. The methodology utilises the conditional generative adversarial network and auxiliary information pertaining to failure modes to control and direct the failure data generation process. The theoretical foundation of the methodology in a non-parametric setting is presented and we show that it holds in practice using empirical results. The methodology is evaluated in a real-world case study involving the prediction of air purge valve failures in heavy-trucks. Two prognostics models are developed using the gradient boosting machine and random forest classifiers. When these models are trained on the augmented training dataset, they outperformed the best solution previously proposed in the literature for the case study by a large margin. More specifically, costs due to breakdowns and false alarms are reduced by 44%.

Place, publisher, year, edition, pages
2019. Vol. 7, p. 183996-184007
Keywords [en]
Equipment prognostics, expert knowledge, generative modeling, limited failure data, physics of failure
National Category
Computer Sciences
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-178730DOI: 10.1109/ACCESS.2019.2960310OAI: oai:DiVA.org:su-178730DiVA, id: diva2:1391005
Available from: 2020-02-03 Created: 2020-02-03 Last updated: 2020-02-05Bibliographically approved

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