Fault detection in propulsion motors in the presence of concept drift

TL;DR

This paper introduces a machine learning-based fault detection method for marine propulsion motors that effectively handles concept drift, enabling early overheating detection without full model retraining, outperforming traditional threshold-based systems.

Contribution

The paper presents a novel machine learning approach for detecting motor faults that maintains performance despite concept drift, using simulated and real data for validation.

Findings

Early detection of overheating compared to conventional methods

Effective handling of concept drift without full model retraining

Proven performance on real and simulated operational data

Abstract

Machine learning and statistical methods can improve conventional motor protection systems, providing early warning and detection of emerging failures. Data-driven methods rely on historical data to learn how the system is expected to behave under normal circumstances. An unexpected change in the underlying system may cause a change in the statistical properties of the data, and by this alter the performance of the fault detection algorithm in terms of time to detection and false alarms. This kind of change, called \textit{concept drift}, requires adaptations to maintain constant performance. In this article, we present a machine learning approach for detecting overheating in the stator windings of marine electrical propulsion motors. Using simulated overheating faults injected into operational data, the methods are shown to provide early detection compared to conventional methods based on temperature readings and fixed limits. The proposed monitors are designed to operate for a type of concept drift observed in operational data collected from a specific class of motors in a fleet of ships. Using a mix of real and simulated concept drifts, it is shown that the proposed monitors are able to provide early detections during and after concept drifts, without the need for full model retraining.