An Integrated Uncertainty Quantification Model for Longitudinal and Time-to-Event Data

We present a novel joint prognostic framework for the integrated analysis and uncertainty quantification of longitudinal (i.e., multi-sensor degradation signals) data and time-to-event data. Specifically, the proposed method models longitudinal data using a functional principal component analysis (F...

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Veröffentlicht in:	IEEE transactions on automation science and engineering 2024-07, p.1-14
Hauptverfasser:	Huh, Ye Kwon, Kim, Minhee, Liu, Kaibo, Zhou, Shiyu
Format:	Artikel
Sprache:	eng
Schlagworte:	Analytical models Artificial neural networks Bayesian neural network Cox proportional hazards model Data models Degradation Eigenvalues and eigenfunctions functional principal component analysis joint event model Predictive models remaining useful life Uncertainty
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