Machine learning based concept drift detection for predictive maintenance

In this work we present a machine learning based approach for detecting drifting behavior - so-called concept drifts - in continuous data streams. The motivation for this contribution originates from the currently intensively investigated topic Predictive Maintenance (PdM), which refers to a proactive way of triggering servicing actions for industrial machinery. The aim of this maintenance strategy is to identify wear and tear, and consequent malfunctioning by analyzing condition monitoring data, recorded by sensor equipped machinery, in real-time. Recent developments in this area have shown potential to save time and material by preventing breakdowns and improving the overall predictability of industrial processes. However, due to the lack of high quality monitoring data and only little experience concerning the applicability of analysis methods, real-world implementations of Predictive Maintenance are still rare. Within this contribution, we present a method, to detect concept drift in data streams as potential indication for defective system behavior and depict initial tests on synthetic data sets. Further on, we present a real-world case study with industrial radial fans and discuss promising results gained from applying the detailed approach in this scope.