Real-time prediction of propulsion motor overheating using machine learning

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 minutes before failure occurs, and it is able to detect the emerging fault at temperatures below the current alarm limits.

Comparative Analysis of Parametric and Non-Parametric Data-Driven Models to Predict Road Crash Severity among Elderly Drivers Using Synthetic Resampling Techniques

“…Machine learning models have emerged as powerful tools for analyzing complex datasets and predicting outcomes across diverse sectors [18][19][20][21]. Their application to road safety, as demonstrated in this study, enables a comprehensive understanding of factors contributing to crash severity [16,[22][23][24][25][26][27][28][29].…”