Ensuring the reliability of power electronic converters is a matter of great importance, and data-driven condition monitoring techniques are cementing themselves as an important tool for this purpose. However, translating methods that work well in controlled lab environments to field applications presents significant challenges, notably because of the limited diversity and accuracy of the lab training data. By enabling the use of field data, online machine learning can be a powerful tool to overcome this problem, but it introduces additional challenges in ensuring the stability and predictability of the training processes. This work presents an edge computing method that mitigates these shortcomings with minimal additional memory usage, by employing an autonomous algorithm that prioritizes the storage of training samples with larger prediction errors. The method is demonstrated on the use case of a self-commissioning condition monitoring system, in the form of a thermal anomaly detection scheme for a variable frequency motor drive, where the algorithm self-learned to distinguish normal and anomalous operation with minimal prior knowledge. The obtained results, based on experimental data, show a significant improvement in prediction accuracy and training speed, when compared to equivalent models trained online without the proposed data selection process.

翻译：确保电力电子变换器的可靠性至关重要，而数据驱动状态监测技术正成为实现此目标的重要工具。然而，将在受控实验室环境中表现良好的方法转化为现场应用面临着重大挑战，尤其是由于实验室训练数据的多样性和准确性有限。通过利用现场数据，在线机器学习可以成为克服这一问题的有力工具，但它在确保训练过程的稳定性和可预测性方面引入了更多挑战。本文提出了一种边缘计算方法，通过采用自主算法优先存储具有较大预测误差的训练样本，以最小的额外内存使用量缓解这些不足。该方法在一个自部署状态监测系统的用例中得到了验证，具体表现为变频电机驱动的热异常检测方案，该算法在极少先验知识的情况下自主学习区分正常与异常运行。基于实验数据的获得结果表明，与未采用所提数据选择过程的在线训练等效模型相比，本方法在预测精度和训练速度上均有显著提升。