概念漂移存在下的推进电机故障检测
Fault detection in propulsion motors in the presence of concept drift
AI总结：
提出一种机器学习方法用于检测船舶推进电机定子绕组过热，该方法在概念漂移发生期间和之后均能提供早期检测，且无需完整的模型重训练。
摘要：
机器学习和统计方法可以改进传统的电机保护系统，提供对新兴故障的早期预警和检测。数据驱动方法依赖历史数据来学习系统在正常情况下的预期行为。底层系统的意外变化可能导致数据统计特性发生改变，从而改变故障检测算法在检测时间和误报方面的性能。这种变化被称为概念漂移，需要进行适应以维持稳定的性能。在本文中，我们提出了一种机器学习方法，用于检测船舶电力推进电机定子绕组的过热现象。通过将模拟的过热故障注入运行数据中，结果表明，与基于温度读数和固定限制的传统方法相比，这些方法能够提供早期检测。所提出的监测器专为在船队中特定类别电机收集的运行数据中观察到的一类概念漂移而设计。通过结合真实和模拟的概念漂移，结果表明所提出的监测器能够在概念漂移发生期间和之后提供早期检测，而无需进行完整的模型重训练。
英文摘要：
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 \textit{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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
