QUANTILE-BASED LSTM REMAINING USEFUL LIFE PREDICTOR

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dc.contributor.advisorMcCluskey, Patrick PMen_US
dc.contributor.authorsaadon, yonatanen_US
dc.contributor.departmentMechanical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2022-02-04T06:36:09Z
dc.date.available2022-02-04T06:36:09Z
dc.date.issued2021en_US
dc.description.abstractAccurate prediction of the remaining useful life (RUL) of a degrading component is crucial to prognostics and health management for electronic systems, to monitor conditions and avoid reaching failure while minimizing downtime. However, the shortage of sufficiently large run-to-failure datasets is a serious bottleneck impeding the performance of data-driven approaches, and in particular, those involving neural network architectures. Here, this work shows a new data-driven prognostic method to predict the RUL using an ensemble of quantile-based Long Short-Term Memory (LSTM) neural networks, which represents the RUL prediction task to a set of simpler, binary classification problems that are amenable for prediction with LSTMs, even with limited data. This methodology was tested on two run-to-failure datasets, power MOSFETs and filtration system, and showed promising results on both datasets it demonstrates that this approach obtains improved RUL estimation accuracy for both the power MOSFETs and the filtration system, especially with a small training dataset that is characterized by a wide range of the RUL.en_US
dc.identifierhttps://doi.org/10.13016/fsxh-9g2x
dc.identifier.urihttp://hdl.handle.net/1903/28439
dc.language.isoenen_US
dc.subject.pqcontrolledArtificial intelligenceen_US
dc.subject.pqcontrolledReligious educationen_US
dc.titleQUANTILE-BASED LSTM REMAINING USEFUL LIFE PREDICTORen_US
dc.typeDissertationen_US

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