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

TL;DR

This paper presents a framework for automatically predicting degradation stages of bearings in pharmaceutical manufacturing using high-frequency vibration data and machine learning, aiding predictive maintenance and regulatory compliance.

Contribution

It introduces a novel k-means segmentation method combined with AutoEncoder embedding for bearing degradation prediction, advancing component-level maintenance strategies.

Findings

Framework is scalable across different bearings

Provides reliable degradation stage predictions

Effective use of high-frequency vibration data

Abstract

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.