ABSTRACT

Thermal protection in marine electrical propulsion motors is commonly implemented by installing temperature sensors on the windings of the motor. An alarm is issued once the temperature reaches the alarm limit, while the motor shuts down once the trip limit is reached. Field experience shows that this protection scheme in some cases is insufficient, as the motor may already be damaged before reaching the trip limit. In this paper, we develop a machine learning algorithm to predict overheating, based on past data collected from a class of identical vessels. All methods were implemented to comply with real-time requirements of the on-board protective systems with minimal need for memory and computational power. Our two-stage overheating detection algorithm first predicts the temperature in a normal state using linear regression fitted to regular operation motor performance measurements, with exponentially smoothed predictors accounting for time dynamics. Then it identifies and monitors temperature deviations between the observed and predicted temperatures using an adaptive cumulative sum (CUSUM) procedure. Using data from a real fault case, the monitor alerts between 60 to 90 min before failure occurs, and it is able to detect the emerging fault at temperatures below the current alarm limits.

摘要

船用电力推进电机的热保护通常通过在电机绕组上安装温度传感器来实现。一旦温度达到警报限值，就会发出警报，而一旦达到跳闸限值，电机就会关闭。现场经验表明，这种保护方案在某些情况下是不够的，因为电机可能在达到跳闸限制之前就已经损坏。在本文中，我们开发了一种机器学习算法来预测过热，该算法基于从一类相同容器中收集的过去数据。所有方法的实施都符合机载保护系统的实时要求，对内存和计算能力的需求最小。我们的两阶段过热检测算法首先使用线性回归预测正常状态下的温度，该线性回归适用于常规操作电机性能测量，指数平滑预测因子考虑了时间动态。然后，它使用自适应累积和 (CUSUM) 程序识别和监测观测温度和预测温度之间的温度偏差。使用来自真实故障案例的数据，监控器会在故障发生前 60 到 90 分钟发出警报，并且能够在温度低于当前警报限值时检测到新出现的故障。