Paper on Unscented Kalman Filtering for Prognostics

by Thom Badings

A paper co-authored by PrimaVera researchers Luc Keizers and Prof. Tiedo Tinga was recently published in the International Journal of Prognostics and Health Management (PHM). In this paper, our researchers propose Unscented Kalman Filtering as a method for performing prognostics under varying operational and environmental conditions. In a series of experiments, the advantages of using Unscented Kalman Filtering as a hybrid prognostic tool are demonstrated, especially when dealing with uncertain operational and environmental conditions.

Interested to learn more about this work? Click here to read the full article!

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