  In the pharmaceutical industry, the maintenance of production machines must
be audited by the regulator. In this context, the problem of predictive
maintenance is not when to maintain a machine, but what parts to maintain at a
given point in time. The focus shifts from the entire machine to its component
parts and prediction becomes a classification problem. In this paper, we focus
on rolling-elements bearings and we propose a framework for predicting their
degradation stages automatically. Our main contribution is a k-means bearing
lifetime segmentation method based on high-frequency bearing vibration signal
embedded in a latent low-dimensional subspace using an AutoEncoder. Given
high-frequency vibration data, our framework generates a labeled dataset that
is used to train a supervised model for bearing degradation stage detection.
Our experimental results, based on the FEMTO Bearing dataset, show that our
framework is scalable and that it provides reliable and actionable predictions
for a range of different bearings.
