This work presents a novel semi-supervised learning approach for data-driven
modeling of asset failures when health status is only partially known in
historical data. We combine a generative model parameterized by deep neural
networks with non-linear embedding technique. It allows us to build prognostic
models with the limited amount of health status information for the precise
prediction of future asset reliability. The proposed method is evaluated on a
publicly available dataset for remaining useful life (RUL) estimation, which
shows significant improvement even when a fraction of the data with known
health status is as sparse as 1% of the total. Our study suggests that the
non-linear embedding based on a deep generative model can efficiently
regularize a complex model with deep architectures while achieving high
prediction accuracy that is far less sensitive to the availability of health
status information.

本研究提出了一种基于深度神经网络参数化的生成模型和非线性嵌入技术的半监督学习方法，用于在历史数据中只部分了解健康状况的情况下建立资产故障的数据驱动建模。该方法可以使用有限的健康状况信息构建预测模型，精确预测未来资产的可靠性，实验结果表明深度生成模型所基于的非线性嵌入技术可以有效规范复杂模型，并实现高预测精度，不受健康状况信息可用性的影响。