A Self-Commissioning Edge Computing Method for Data-Driven Anomaly Detection in Power Electronic Systems

TL;DR

This paper introduces a self-commissioning edge computing approach for data-driven anomaly detection in power electronic systems, enhancing online learning stability and accuracy with minimal memory overhead.

Contribution

It proposes an autonomous data selection algorithm that improves online machine learning for field condition monitoring of power electronics.

Findings

Significant accuracy improvement over traditional online models.

Faster training speed with minimal memory use.

Effective anomaly detection in real-world experiments.

Abstract

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.