Contending Remaining Useful Life Algorithms

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...

Bayesian Approach for Remaining Useful Life Prediction

Prediction of the remaining useful life (RUL) of critical components is a non-trivial task for industrial applications. RUL can differ for similar components operating under the same conditions. Working with such problem, one needs to contend with many uncertainty sources such as system, model and sensory noise. To do that, proposed models should include such uncertainties and represent the bel...

Impact of Input Uncertainty on Failure Prognostic Algorithms: Extending the Remaining Useful Life of Nonlinear Systems

This paper presents a novel set of uncertainty measures to quantify the impact of input uncertainty on nonlinear prognosis systems. A Particle Filtering-based method is also presented that uses this set of uncertainty measures to quantify, in real time, the impact of load, environmental, and other stresses for long-term prediction. Furthermore, this work shows how these measures can be used to ...

Comparison of Prognostic Algorithms for Estimating Remaining Useful Life of Batteries

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. RUL prediction needs to contend with multiple sources of errors like modeling inconsistencies, system noise and degraded sensor fidelity, which leads...

A Bayesian Framework for Remaining Useful Life Estimation