  Machine learning and statistical methods can be used to enhance monitoring
and fault prediction in marine systems. These methods rely on a dataset with
records of historical system behaviour, potentially containing periods of both
fault-free and faulty operation. An unexpected change in the underlying system,
called a concept drift, may impact the performance of these methods, triggering
the need for model retraining or other adaptations. In this article, we present
an approach for detecting overheating in stator windings of marine propulsion
motors that is able to successfully operate during concept drift without the
need for full model retraining. Two distinct approaches are presented and
tested. All models are trained and verified using a dataset from operational
propulsion motors, with known, sudden concept drifts.
