用重整化群思想构建可解释的故障诊断网络，解决类别不平衡问题
Neural Network Implementation of the Renormalization Group for Fault Diagnosis with Class Imbalance
- 基于重整化群思想分层压缩特征空间，保留多尺度信息
- 在AI4I数据集上达到优于传统方法的分类性能
- 可视化显示离散曲线结构，证明粗粒化有效，适合工业故障诊断
机器学习在实际应用中面临类别不平衡和多维噪声的挑战。本文提出RGNet，一种基于重整化群（RG）概念的神经网络架构，用于特征空间的分层粗粒化。该模型依次压缩输入维度，并在分类前拼接所有尺度的信息，从而捕捉局部细节与全局模式。引入了RG流的概念——可解释的低维表示，通过t-SNE可视化揭示出离散的曲线结构，验证了粗粒化的有效性。在类别不平衡的AI4I数据集上的实验结果表明，RGNet是一种通用、可解释且具有竞争力的故障预测方案。
原文摘要 · Abstract (English)
The application of machine learning models in practical tasks faces challenges such as class imbalance and multidimensional noise. This paper proposes RGNet, a neural network architecture based on the concept of the renormalization group (RG), for hierarchical coarse-graining of the feature space. The model sequentially compresses the input dimensionality and concatenates all scales before classification, allowing it to capture both local details and global patterns. The notion of RG-flows is introduced - interpretable low-dimensional representations whose visualization via t-SNE reveals a discrete curvilinear structure confirming the effectiveness of coarse-graining. Experimental results are presented on the imbalanced AI4I dataset. The obtained results demonstrate that RGNet is a universal, interpretable, and competitive solution for fault prediction in applications with imbalanced classes.
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