Remaining Useful Life Prediction for a Catenary, Utilizing Bayesian Optimization of Stacking

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...

A Neural Network Approach for Remaining Useful Life Prediction Utilizing Both Failure and Suspension Histories

Artificial neural network (ANN) methods have shown great promise in achieving more accurate equipment remaining useful life prediction. However, most reported ANN methods only utilize condition monitoring data from failure histories, and ignore data obtained from suspension histories in which equipments are taken out of service before they fail. Suspension history condition monitoring data cont...

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...

A Study on Remaining Useful Life Prediction for Prognostic Applications

We consider the prediction algorithm and performance evaluation for prognostics and health management (PHM) problems, especially the prediction of remaining useful life (RUL) for the milling machine cutter and lithium‐ion battery. We modeled battery as a voltage source and internal resisters. By analyzing voltage change trend during discharge, we made the prediction of battery remain discharge ...