Deep learning techniques have become one of the main propellers for solving engineering problems effectively and efficiently. For instance, Predictive Maintenance methods have been used to improve predictions of when maintenance is needed on different machines and operative contexts. However, deep learning methods are not without limitations, as these models are normally trained on a fixed distribution that only reflects the current state of the problem. Due to internal or external factors, the state of the problem can change, and the performance decreases due to the lack of generalization and adaptation. Contrary to this stationary training set, real-world applications change their environments constantly, creating the need to constantly adapt the model to evolving scenarios. To aid in this endeavor, Continual Learning methods propose ways to constantly adapt prediction models and incorporate new knowledge after deployment. Despite the advantages of these techniques, there are still challenges to applying them to real-world problems. In this work, we present a brief introduction to predictive maintenance, non-stationary environments, and continual learning, together with an extensive review of the current state of applying continual learning in real-world applications and specifically in predictive maintenance. We then discuss the current challenges of both predictive maintenance and continual learning, proposing future directions at the intersection of both areas. Finally, we propose a novel way to create benchmarks that favor the application of continuous learning methods in more realistic environments, giving specific examples of predictive maintenance.

翻译：深度学习技术已成为高效解决工程问题的主要驱动力之一。例如，预测性维护方法已被用于改进对不同机器及运行环境下维护时机的预测精度。然而，深度学习方法并非没有局限——此类模型通常基于反映当前问题状态的固定分布进行训练。受内外部因素影响，问题状态可能发生变化，导致模型因缺乏泛化与适应能力而性能下降。与此类静态训练集相反，现实应用场景的环境处于持续变动中，亟需模型不断适应动态情境。为应对这一挑战，持续学习方法提出了模型部署后持续调整预测模型并融入新知识的方案。尽管这些技术具有优势，但在实际问题中的应用仍面临诸多挑战。本文首先简要介绍预测性维护、非平稳环境与持续学习的基本概念，继而全面综述持续学习在现实应用（特别是预测性维护领域）中的研究现状。我们进一步探讨预测性维护与持续学习当前面临的挑战，并提出两领域交叉方向的未来研究路径。最后，我们提出一种创新的基准构建方法，旨在促进持续学习方法在更真实环境中的应用，并通过预测性维护的具体案例进行阐释。