Prognostics of rotating machines through generalized Gaussian hidden Markov models

Nowadays, the industrial scenario is driven by the need of costs and time reduction. In this contest, system failure prediction plays a pivotal role in order to program maintenance operations only in the last stages of the real operating life, avoiding unnecessary machine downtime. In the last decade, Hidden Markov Models have been widely exploited for machinery prognostic purposes. The probabilistic dependency between the measured observations and the real damaging stage of the system has usually been described as a mixture of Gaussian distributions. This paper aims to generalize the probabilistic function as a mixture of generalized Gaussian distributions in order to consider possible distribution variations during the different states. In this direction, this work proposes an algorithm for the estimation of the model parameters exploiting the observations measured on the real system. The prognostic effectiveness of the resulting model has been demonstrated through the analysis of several run-to-failure datasets concerning both rolling element bearings and more complex systems.