Data-driven prognosis: a multi-physics approach verified via balloon burst experiment.

Abstract

A multi-physics formulation for data-driven prognosis (DDP) is developed. Unlike traditional predictive strategies that require controlled offline measurements or 'training' for determination of constitutive parameters to derive the transitional statistics, the proposed DDP algorithm relies solely on in situ measurements. It uses a deterministic mechanics framework, but the stochastic nature of the solution arises naturally from the underlying assumptions regarding the order of the conservation potential as well as the number of dimensions involved. The proposed DDP scheme is capable of predicting onset of instabilities. Because the need for offline testing (or training) is obviated, it can be easily implemented for systems where such a priori testing is difficult or even impossible to conduct. The prognosis capability is demonstrated here via a balloon burst experiment where the instability is predicted using only online visual observations. The DDP scheme never failed to predict the incipient failure, and no false-positives were issued. The DDP algorithm is applicable to other types of datasets. Time horizons of DDP predictions can be adjusted by using memory over different time windows. Thus, a big dataset can be parsed in time to make a range of predictions over varying time horizons.