用生成模型补数据，提升小样本下的元器件停产预测精度
Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications
- 用深度生成模型合成新停产案例，扩充稀缺训练数据
- 在基准数据集上达到当前最优预测性能
- 适合数据少的工业场景，如长寿命设备维护
电子元器件停产问题在长生命周期系统中尤为严峻。为应对这一挑战，停产预测成为关键手段，已有多种基于机器学习的方法被提出。然而，现有方法依赖大量相关数据以实现高精度，而实际应用中常面临数据匮乏。本文提出一种基于深度学习的停产预测新框架，通过深度生成建模合成新的停产案例，用于扩充训练数据集。该增强数据集随后用于训练经典机器学习预测模型。为在增强数据上训练传统监督学习分类器，本文将经典分类器改造为半监督学习范式。该框架在多个基准数据集上表现优异，达到当前最优水平。
原文摘要 · Abstract (English)
The challenge of electronic component obsolescence is particularly critical in systems with long life cycles. Various obsolescence management methods are employed to mitigate its impact, with obsolescence forecasting being a highly sought-after and prominent approach. As a result, numerous machine learning-based forecasting methods have been proposed. However, machine learning models require a substantial amount of relevant data to achieve high precision, which is lacking in the current obsolescence landscape in some situations. This work introduces a novel framework for obsolescence forecasting based on deep learning. The proposed framework solves the lack of available data through deep generative modeling, where new obsolescence cases are generated and used to augment the training dataset. The augmented dataset is then used to train a classical machine learning-based obsolescence forecasting model. To train classical forecasting models using augmented datasets, existing classical supervised-learning classifiers are adapted for semi-supervised learning within this framework. The proposed framework demonstrates state-of-the-art results on benchmarking datasets.
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