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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Ball bearings find widespread use in various manufacturing and mechanicaldomains, and methods based on machine learning have been widely adopted in thefield to monitor wear and spot defects before they lead to failures. Fewstudies, however, have addressed the problem of censored data, in which failureis not observed. In this paper, we propose a novel approach to predict the timeto failure in ball bearings using survival analysis. First, we analyze bearingdata in the frequency domain and annotate when a bearing fails by comparing theKullback-Leibler divergence and the standard deviation between its break-infrequency bins and its break-out frequency bins. Second, we train severalsurvival models to estimate the time to failure based on the annotated data andcovariates extracted from the time domain, such as skewness, kurtosis andentropy. The models give a probabilistic prediction of risk over time and allowus to compare the survival function between groups of bearings. We demonstrateour approach on the XJTU and PRONOSTIA datasets. On XJTU, the best result is a0.70 concordance-index and 0.21 integrated Brier score. On PRONOSTIA, the bestis a 0.76 concordance-index and 0.19 integrated Brier score. Our work motivatesfurther work on incorporating censored data in models for predictivemaintenance.

Predicting Survival Time of Ball Bearings in the Presence of Censoring