Explainable Artificial Intelligence based Soft Evaluation Indicator for Arc Fault Diagnosis

TL;DR

This paper introduces a soft evaluation indicator for AI-based arc fault diagnosis models, enhancing interpretability and trustworthiness by explaining model outputs and ensuring reliable fault detection.

Contribution

It proposes a novel explainability method and a lightweight neural network to improve trust and understanding of arc fault diagnosis models.

Findings

The soft evaluation indicator effectively explains model outputs.

The lightweight neural network maintains high accuracy with better interpretability.

Models tested across diverse datasets show improved trustworthiness.

Abstract

Novel AI-based arc fault diagnosis models have demonstrated outstanding performance in terms of classification accuracy. However, an inherent problem is whether these models can actually be trusted to find arc faults. In this light, this work proposes a soft evaluation indicator that explains the outputs of arc fault diagnosis models, by defining the the correct explanation of arc faults and leveraging Explainable Artificial Intelligence and real arc fault experiments. Meanwhile, a lightweight balanced neural network is proposed to guarantee competitive accuracy and soft feature extraction score. In our experiments, several traditional machine learning methods and deep learning methods across two arc fault datasets with different sample times and noise levels are utilized to test the effectiveness of the soft evaluation indicator. Through this approach, the arc fault diagnosis models are easy to understand and trust, allowing practitioners to make informed and trustworthy decisions.