Predicting Survival Time of Ball Bearings in the Presence of Censoring
Christian Marius Lillelund Fernando Pannullo Morten Opprud Jakobsen Christian Fischer Pedersen
Ball bearings find widespread use in various manufacturing and mechanical
domains, and methods based on machine learning have been widely adopted in the
field to monitor wear and spot defects before they lead to failures. Few
studies, however, have addressed the problem of censored data, in which failure
is not observed. In this paper, we propose a novel approach to predict the time
to failure in ball bearings using survival analysis. First, we analyze bearing
data in the frequency domain and annotate when a bearing fails by comparing the
Kullback-Leibler divergence and the standard deviation between its break-in
frequency bins and its break-out frequency bins. Second, we train several
survival models to estimate the time to failure based on the annotated data and
covariates extracted from the time domain, such as skewness, kurtosis and
entropy. The models give a probabilistic prediction of risk over time and allow
us to compare the survival function between groups of bearings. We demonstrate
our approach on the XJTU and PRONOSTIA datasets. On XJTU, the best result is a
0.70 concordance-index and 0.21 integrated Brier score. On PRONOSTIA, the best
is a 0.76 concordance-index and 0.19 integrated Brier score. Our work motivates
further work on incorporating censored data in models for predictive
maintenance.