论文标题
预测维护的深度学习模型：调查，比较，挑战和前景
Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect
论文作者
论文摘要
鉴于全球范围内越来越多的工业数据空间，深度学习解决方案已成为预测维护的流行，这些维护是监控资产以优化维护任务的。考虑到文献中发现的示例数量，为每个用例选择最合适的架构是复杂的。这项工作旨在通过审查最先进的深度学习体系结构以及如何与预测性维护阶段集成以满足工业公司的要求（即异常检测，根本原因分析，保持有用的寿命估算），旨在促进这项任务。它们在工业应用中进行了分类和比较，并解释了如何填补空白。最后，提出了公开的挑战和未来的研究途径。
Given the growing amount of industrial data spaces worldwide, deep learning solutions have become popular for predictive maintenance, which monitor assets to optimise maintenance tasks. Choosing the most suitable architecture for each use-case is complex given the number of examples found in literature. This work aims at facilitating this task by reviewing state-of-the-art deep learning architectures, and how they integrate with predictive maintenance stages to meet industrial companies' requirements (i.e. anomaly detection, root cause analysis, remaining useful life estimation). They are categorised and compared in industrial applications, explaining how to fill their gaps. Finally, open challenges and future research paths are presented.
