Data Driven Prognosis: A multi-physics approach verified via balloon burst experiment

TL;DR

This paper introduces a data-driven prognosis method that uses only in situ measurements within a multi-physics framework to predict system instabilities, demonstrated through a balloon burst experiment without prior offline testing.

Contribution

The paper presents a novel, purely data-driven prognosis algorithm that predicts instabilities using in situ measurements, eliminating the need for offline training or controlled experiments.

Findings

Successfully predicted balloon burst using only visual observations

Never failed to predict incipient failure in tested cases

No false positives issued during predictions

Abstract

A multi-physics formulation for Data Driven Prognosis (DDP) is developed. Unlike traditional predictive strategies that require controlled off-line measurements or training for determination of constitutive parameters to derive the transitional statistics, the proposed DDP algorithm relies solely on in situ measurements. It utilizes 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. Since the need for off-line 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 utilizing only on-line visual observations. The DDP scheme never failed to predict the incipient failure, and no false positives were issued. The DDP algorithm is applicable to others 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.