Deep Learning Approaches to Remaining Useful Life Prediction: A Survey

Logan Cummins, Brad Killen, Kirby Thomas, Paul Barrett, Shahram Rahimi, Maria Seale
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引用次数: 4

Abstract

Prognostic and Health Management (PHM) systems have multiple facets one would need to perfect for an efficient system. One of these is the prediction of remaining useful life (RUL), which is the task of producing a number of time units (cycles, minutes, days, etc) until a part of the system or the system as a whole will fail. Over the years, deep learning approaches have been used to effectively perform this task, and these approaches fall into multiple different types of deep learning architectures. While non deep learning approaches exist, this paper focuses on a number of different deep learning approaches to solving the problem of RUL prediction.
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剩余使用寿命预测的深度学习方法综述
预后和健康管理(PHM)系统有多个方面,人们需要完善一个有效的系统。其中之一是剩余使用寿命(RUL)的预测,这是产生一些时间单位(周期、分钟、天等)的任务,直到系统的一部分或整个系统失效。多年来,深度学习方法已被用于有效地执行此任务,并且这些方法属于多种不同类型的深度学习架构。虽然存在非深度学习方法,但本文重点介绍了一些不同的深度学习方法来解决RUL预测问题。
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