用可解释AI构建软性评估指标，提升电弧故障诊断可信度
Explainable Artificial Intelligence based Soft Evaluation Indicator for Arc Fault Diagnosis
- 基于可解释AI和真实实验定义电弧故障的正确解释标准
- 在两个数据集上验证，模型准确率与特征提取能力均达良好水平
- 适合需高可信度诊断的工业场景，如电力系统安全监测
基于AI的电弧故障诊断模型虽在分类准确率上表现优异，但其可信赖性存疑。本文提出一种软性评估指标，通过定义电弧故障的正确解释方式，并结合可解释人工智能与真实电弧故障实验，实现对模型输出的可解释性评估。同时设计了一种轻量级平衡神经网络，在保证竞争力准确率的同时，提升软特征提取得分。在两个具有不同采样时间与噪声水平的电弧故障数据集上，测试了多种传统机器学习与深度学习方法的有效性。该方法使诊断模型更易理解与信任，助力从业者做出可靠决策。
原文摘要 · Abstract (English)
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.
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