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

Predicting Bearings' Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry