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.

翻译：机器学习和统计方法可用于增强船舶系统的监测与故障预测能力。这些方法依赖于包含历史系统行为记录的数据集，其中可能同时包含正常运行与故障运行时段。系统底层发生的意外变化（称为概念漂移）可能影响这些方法的性能，从而触发模型重新训练或其他适应性调整的需求。本文提出一种船舶推进电机定子绕组过热检测方法，该方法能够在概念漂移期间持续有效运行，且无需完整的模型重新训练。文中提出并测试了两种不同的技术路径。所有模型均使用来自实际运行推进电机的数据集进行训练与验证，该数据集包含已知的突发性概念漂移。