English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The main objective of Prognostics and Health Management is to estimate theRemaining Useful Lifetime (RUL), namely, the time that a system or a piece ofequipment is still in working order before starting to function incorrectly. Inrecent years, numerous machine learning algorithms have been proposed for RULestimation, mainly focusing on providing more accurate RUL predictions.However, there are many sources of uncertainty in the problem, such as inherentrandomness of systems failure, lack of knowledge regarding their future states,and inaccuracy of the underlying predictive models, making it infeasible topredict the RULs precisely. Hence, it is of utmost importance to quantify theuncertainty alongside the RUL predictions. In this work, we investigate theconformal prediction (CP) framework that represents uncertainty by predictingsets of possible values for the target variable (intervals in the case of RUL)instead of making point predictions. Under very mild technical assumptions, CPformally guarantees that the actual value (true RUL) is covered by thepredicted set with a degree of certainty that can be prespecified. We studythree CP algorithms to conformalize any single-point RUL predictor and turn itinto a valid interval predictor. Finally, we conformalize two single-point RULpredictors, deep convolutional neural networks and gradient boosting, andillustrate their performance on the Commercial Modular Aero-Propulsion SystemSimulation (C-MAPSS) data sets.

Conformal Prediction Intervals for Remaining Useful Lifetime Estimation