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.

通过利用边缘计算方法，使用自主算法来优化存储具有更大预测误差的训练样本，解决电力电子变换器可靠性的问题，并在自动感知条件监测系统中进行了实证研究，结果显示与在线训练等等效模型相比，预测准确性和训练速度都有显著改善。