Modelling variability in vibration-based PBSHM via a generalised population form

TL;DR

This paper develops a generalized population model using Gaussian processes to account for variability in vibration data from similar helicopter blades, enhancing the robustness of structural health monitoring.

Contribution

It introduces a novel generalized model for frequency response functions of similar structures using mixtures of Gaussian processes, addressing variability in SHM data.

Findings

Model captures variability among nominally identical blades.

Improves generalization of SHM techniques across similar structures.

Addresses challenges from manufacturing differences and environmental fluctuations.

Abstract

Structural health monitoring (SHM) has been an active research area for the last three decades, and has accumulated a number of critical advances over that period, as can be seen in the literature. However, SHM is still facing challenges because of the paucity of damage-state data, operational and environmental fluctuations, repeatability issues, and changes in boundary conditions. These issues present as inconsistencies in the captured features and can have a huge impact on the practical implementation, but more critically, on the generalisation of the technology. Population-based SHM has been designed to address some of these concerns by modelling and transferring missing information using data collected from groups of similar structures. In this work, vibration data were collected from four healthy, nominally-identical, full-scale composite helicopter blades. Manufacturing differences (e.g., slight differences in geometry and/or material properties), among the blades presented as variability in their structural dynamics, which can be very problematic for SHM based on machine learning from vibration data. This work aims to address this variability by defining a general model for the frequency response functions of the blades, called a form, using mixtures of Gaussian processes.