ArXiv Preprint
Applications of Structural Health Monitoring (SHM) combined with Machine
Learning (ML) techniques enhance real-time performance tracking and increase
structural integrity awareness of civil, aerospace and automotive
infrastructures. This SHM-ML synergy has gained popularity in the last years
thanks to the anticipation of maintenance provided by arising ML algorithms and
their ability of handling large quantities of data and considering their
influence in the problem.
In this paper we develop a novel ML nearest-neighbors-alike algorithm based
on the principle of maximum entropy to predict fatigue damage (Palmgren-Miner
index) in composite materials by processing the signals of Lamb Waves -- a
non-destructive SHM technique -- with other meaningful features such as layup
parameters and stiffness matrices calculated from the Classical Laminate Theory
(CLT). The full data analysis cycle is applied to a dataset of delamination
experiments in composites. The predictions achieve a good level of accuracy,
similar to other ML algorithms, e.g. Neural Networks or Gradient-Boosted Trees,
and computation times are of the same order of magnitude.
The key advantages of our proposal are: (1) The automatic determination of
all the parameters involved in the prediction, so no hyperparameters have to be
set beforehand, which saves time devoted to hypertuning the model and also
represents an advantage for autonomous, self-supervised SHM. (2) No training is
required, which, in an \textit{online learning} context where streams of data
are fed continuously to the model, avoids repeated training -- essential for
reliable real-time, continuous monitoring.
Ismael Ben-Yelun, Miguel Diaz-Lago, Luis Saucedo-Mora, Miguel Angel Sanz, Ricardo Callado, Francisco Javier Montans
2022-10-19
