Abstract

Deep learning has emerged as a powerful tool to model complicated relationships between inputs and outputs in various fields including degradation modeling and prognostics. Existing deep learning-based prognostic approaches are often used in a black-box manner and provide only point estimations of remaining useful life. However, accurate interval estimations of the remaining useful life are crucial to understand the stochastic nature of degradation processes and perform reliable risk analysis and maintenance decision making. This study proposes a novel Bayesian deep learning framework that incorporates general characteristics of degradation processes and provides the interval estimations of remaining useful life. The proposed method enjoys several unique advantages: (i) providing a general approach by not assuming any particular type of degradation processes nor the availability of domain-specific prior knowledge such as a failure threshold; (ii) offering the interval estimations of the remaining useful life; (iii) systematically modeling two types of uncertainties embedded in prognostics; and (iv) exhibiting great prognostic performance and wide applicability to complex systems that may involve multiple sensor signals, multiple failure modes, and multiple operational conditions. Numerical studies demonstrate improved prognostic performance and practicality of the proposed method over benchmark approaches. Additional numerical results including the analysis of sensitivity and computational costs are given in the online supplemental materials.

摘要

深度学习已成为一种强大的工具，可以在包括降级建模和预测学在内的各个领域对输入和输出之间的复杂关系进行建模。现有的基于深度学习的预测方法通常以黑盒方式使用，并且仅提供剩余使用寿命的点估计。但是，对剩余使用寿命的准确间隔估计对于了解降解过程的随机性以及执行可靠的风险分析和维护决策至关重要。这项研究提出了一种新颖的贝叶斯深度学习框架，该框架结合了降解过程的一般特征，并提供了剩余使用寿命的区间估计。所提出的方法具有几个独特的优点：（i）通过不假定任何特定类型的降级过程或不提供特定领域的先验知识（例如故障阈值）来提供通用方法；（ii）提供剩余使用寿命的间隔估计；（iii）系统地建模预测中包含的两种不确定性；（iv）具有良好的预后性能，并广泛适用于可能涉及多个传感器信号，多个故障模式和多个运行状况的复杂系统。数值研究表明，与基准方法相比，该方法具有更好的预后性能和实用性。在线补充材料中提供了其他数值结果，包括敏感性分析和计算成本。