Online remaining-useful-life estimation with a Bayesian-updated expectation-conditional-maximization algorithm and a modified Bayesian-model-averaging method

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...

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...

Bayesian K-Means as a "Maximization-Expectation" Algorithm

We introduce a new class of "maximization-expectation" (ME) algorithms where we maximize over hidden variables but marginalize over random parameters. This reverses the roles of expectation and maximization in the classical expectation-maximization algorithm. In the context of clustering, we argue that these hard assignments open the door to very fast implementations based on data structures su...

Bayesian K-Means as a “Maximization-Expectation” Algorithm

We introduce a new class of “maximization expectation” (ME) algorithms where we maximize over hidden variables but marginalize over random parameters. This reverses the roles of expectation and maximization in the classical EM algorithm. In the context of clustering, we argue that these hard assignments open the door to very fast implementations based on data-structures such as kdtrees and cong...