Robust Feature Learning for Remaining Useful Life Estimation Using Siamese Neural Networks

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dc.authorid0000-0001-9213-576X
dc.contributor.authorAydemir, Gurkan
dc.contributor.authorPaynabar, Kamran
dc.contributor.authorAcar, Burak
dc.date.accessioned2026-02-12T21:04:53Z
dc.date.available2026-02-12T21:04:53Z
dc.date.issued2022
dc.departmentBursa Teknik Üniversitesi
dc.description30th European Signal Processing Conference (EUSIPCO) -- AUG 29-SEP 02, 2022 -- Belgrade, SERBIA
dc.description.abstractRemaining useful life (RUL) estimation is very critical for planning the maintenance of machinery in various industries. Deep learning models have gained popularity as the key tools in estimating RUL. However, raw sensor measurements are affected by multiple factors beyond the degradation level, which is the primary goal of RUL estimation. This adversely affects model training, especially in the lack of sufficient data. To address the aforementioned issues, we propose to train an artificial neural network (ANN) that captures the smooth and monotonous degradation process. A siamese neural network (SNN) model is used to train the ANN so as to minimize feature variation across consecutive time instances while maximizing it across changes in health conditions. The effectiveness of the smooth features in RUL estimation is illustrated using turbofan engine degradation simulation data set and degradation image stream data set that are collected from rotating engines. We further discuss that the new features can be individually used to assess the health condition of the machinery without an RUL estimator.
dc.description.sponsorshipEuropean Assoc Signa Proc
dc.identifier.endpage1436
dc.identifier.isbn978-90-827970-9-1
dc.identifier.issn2076-1465
dc.identifier.scopus2-s2.0-85141010608
dc.identifier.scopusqualityN/A
dc.identifier.startpage1432
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6710
dc.identifier.wosWOS:000918827600281
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee
dc.relation.ispartof2022 30Th European Signal Processing Conference (Eusipco 2022)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260212
dc.subjectDeep learning
dc.subjectSiamese Neural Networks
dc.subjectRemaining useful life estimation
dc.subjectMultiple operating conditions
dc.subjectImage prognostics
dc.subjectPredictive maintenance
dc.subjectFeature extraction
dc.titleRobust Feature Learning for Remaining Useful Life Estimation Using Siamese Neural Networks
dc.typeConference Object

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