🤖 AI Summary
To address delayed fault warnings and high false-alarm rates in detecting stator winding overheating in marine electric propulsion motors—caused by concept drift—this paper proposes an adaptive, data-driven monitoring method tailored to the operational characteristics of marine motors. The method introduces the first lightweight, class-level concept-drift detector specifically designed for marine motors, integrating real-world and simulated drift data within an incremental learning and statistical process monitoring framework to avoid full model retraining. Experimental results demonstrate that the proposed approach enables early detection of overheating faults under both synthetic and real-world concept drift scenarios. Compared with conventional temperature-threshold methods, it achieves significantly shorter average warning times while maintaining high detection rates and substantially reducing false alarms. The method exhibits strong adaptability to evolving operating conditions and high engineering practicality for maritime applications.
📝 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 extit{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.