打破同质性假设：用于预测性维护中罕见故障检测的专门多生成器对抗学习
Breaking the Homogeneity Assumption: Specialized Multi-Generator Adversarial Learning for Rare Failure Detection in Predictive Maintenance
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中文总结 AI 辅助
研究预测性维护中罕见故障检测，提出故障类型感知生成增强程序，采用专门多生成器GAN架构，通过防泄漏实验设计比较多种不平衡处理方法，实验表明该框架能产生更真实少数样本，提升PR-AUC和召回分数。
中文摘要 AI 辅助
预测性维护领域的监督学习模型通常在高度不平衡的工业数据集上进行训练，机器故障很少发生，但对运营有不成比例的影响。除了明显的类别差异，故障数据通常是非同质的。传统的不平衡管理方法效果有限。本文确定了一种故障类型感知生成增强程序改善预测性维护系统中罕见故障识别的可能性。使用一种防泄漏的实验设计比较了五种不平衡处理方法，通过精确率/召回率导向的度量来量化模型性能。在AI4I 2020预测性维护数据集上的实验表明，所提出的多生成器GAN框架产生了更真实的少数样本，与传统重采样方法和单生成器GAN增强相比，具有更高的PR-AUC和召回分数。
英文摘要
Supervised learning models in the predictive maintenance field are regularly trained on highly imbalanced industrial datasets: machine failures occur rarely but have a disproportionate effect on operations. In addition to the clear class disparity, failure data are typically non-homogeneous, with different failure modes arising from distinct physical processes and exhibiting a multimodal distribution across minorities and classes. Traditional imbalance-management methods, e.g., undersampling, SMOTE-based interpolation, or cost-sensitive learning, typically assume that the minority population is homogeneous. This means their effectiveness is severely limited in the multifaceted conditions encountered in industrial practice. This paper determines the possibility of a failure-type-conscious generative augmentation program to improve the identification of infrequent failures in predictive maintenance systems. An experimental design that is leakage-safe is used to compare five imbalance-handling methods: cost-sensitive learning, random undersampling, SMOTE oversampling, single-generator GAN augmentation, and a specialized multi-generator GAN architecture that has independent generators that are asked to learn individual failure subtypes. Precision/Recall-oriented measures are used to quantify model performance; the main evaluation measure is the PR-AUC. Experiments conducted on the AI4I 2020 predictive maintenance dataset indicate that the proposed multi-generator GAN framework produces more realistic minority samples, yielding higher PR-AUC and recall scores compared to traditional resampling methods and individual-generator GAN augmentation.
