A data-driven framework for remaining useful life estimation

A Bayesian Framework for Remaining Useful Life Estimation

A Bayesian Framework for Remaining Useful Life Estimation

The estimation of remaining useful life (RUL) of a faulty component is at the center of system prognostics and health management. It gives operators a potent tool in decision making by quantifying how much time is left until functionality is lost. This is especially true for aerospace systems, where unanticipated subsystem downtime may lead to catastrophic failures. RUL prediction needs to cont...

Remaining Useful Life Estimation In the Presence of Given Shocks

In a system, prediction of remaining useful lifetime (RUL) of servicing before reaching to a specified breakdown threshold is a very important practical issue, and research in this field is still regarded as an appreciated research gap. Operational environment of an equipment is not constant and changes regarding to stresses and shocks. These random environmental factors accelerate system deter...

Remaining useful life estimation - A review on the statistical data driven approaches

0377-2217/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.ejor.2010.11.018 ⇑ Corresponding authors at: Salford Business School M5 4WT, UK. Tel.: +44 0161 2954124; fax: +44 0161 2 010 62794461; fax: +86 010 62786911 (D.-H. Zhou). E-mail addresses: [email protected] (W. Wan (D.-H. Zhou). Remaining useful life (RUL) is the useful life left on an asset at a particular time of operation. It...

A Fusion Framework with Nonlinear Degradation Improvement for Remaining Useful Life Estimation of Lithium-ion Batteries

Fusion prognostic framework for lithium-ion battery remaining useful life (RUL) estimation has become a hot spot. Especially, the cycle life prediction has been conducted widely, for which many prognostic methods have been proposed correspondingly. However, many fusion frameworks which can achieve high precision are accompanied with high computing complexity and high time consumption which make...