Being able to predict the remaining useful life (RUL) of an engineering system is an important task in prognostics and health management. Recently, data-driven approaches to RUL predictions are becoming prevalent over model-based approaches since no underlying physical knowledge of the engineering system is required. Yet, this just replaces required expertise of the underlying physics with machine learning (ML) expertise, which is often also not available. Automated machine learning (AutoML) promises to build end-to-end ML pipelines automatically enabling domain experts without ML expertise to create their own models. This paper introduces AutoRUL, an AutoML-driven end-to-end approach for automatic RUL predictions. AutoRUL combines fine-tuned standard regression methods to an ensemble with high predictive power. By evaluating the proposed method on eight real-world and synthetic datasets against state-of-the-art hand-crafted models, we show that AutoML provides a viable alternative to hand-crafted data-driven RUL predictions. Consequently, creating RUL predictions can be made more accessible for domain experts using AutoML by eliminating ML expertise from data-driven model construction.

翻译：能够预测工程系统的剩余使用寿命（RUL）是健康管理与预测性维护中的关键任务。近年来，基于数据驱动的RUL预测方法逐渐取代基于模型的方法，因其无需掌握工程系统的底层物理知识。然而，这仅是将对物理领域专业知识的需求转变为对机器学习（ML）专业知识的需求，而后者往往也难以获得。自动化机器学习（AutoML）旨在自动构建端到端的机器学习流水线，使缺乏ML专业知识的领域专家也能自主创建模型。本文提出AutoRUL——一种基于AutoML驱动的端到端自动RUL预测方法。AutoRUL将经过精细调优的标准回归方法集成为具有高预测能力的集成模型。通过在八个真实与合成数据集上将该方法对比现有最先进的手工构建模型，我们证明AutoML可为手工构建的数据驱动型RUL预测提供可行的替代方案。因此，借助AutoML消除数据驱动模型构建中对ML专业知识的需求，可让领域专家更便捷地完成RUL预测。