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• W4292622317 startingPage "108758" @default.
• W4292622317 abstract "Deep learning (DL) has been intensively exploited for remaining useful life (RUL) prediction in the recent decade. Although with high precision and flexibility, DL methods need sufficient run-to-failure data to guarantee their performance. However, run-to-failure data is fairly expensive to obtain in many industrial applications. How to economically achieve high accuracy with few run-to-failure data becomes a critical and emergent issue. In this study, a Bayesian deep-active-learning framework is proposed for RUL prediction, which goes beyond traditional passive learning and introduces a novel active learning perspective. We use Bayesian neural networks with Monte Carlo dropout inference to predict RUL with uncertainty quantification for samples without run-to-failure labels. The prediction uncertainty is further used to develop an acquisition function for actively selecting target samples to obtain their run-to-failure labels. A recursive model training and active data selection mechanism are then developed to maintain accuracy while reducing the size of the training data. Two practical examples, one from a public bearing dataset and the other from our lab testing on battery degradation, are presented to demonstrate the proposed method. Experimental results demonstrate that 20 and 40% of run-to-failure data can be saved for the bearing and the battery RUL prediction, respectively." @default.
• W4292622317 created "2022-08-22" @default.
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• W4292622317 date "2022-12-01" @default.
• W4292622317 modified "2023-10-16" @default.
• W4292622317 title "Bayesian deep-learning for RUL prediction: An active learning perspective" @default.
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• W4292622317 doi "https://doi.org/10.1016/j.ress.2022.108758" @default.
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