A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes

TL;DR

This paper develops a Gaussian process-based model to represent the normal vibrational behavior of similar structures, accounting for variability, and uses it to detect damage by comparing new data against this generic form.

Contribution

It introduces an overlapping mixture of Gaussian processes (OMGP) to model the normal condition of multiple similar structures, capturing variability and uncertainty for improved damage detection.

Findings

OMGP effectively models variability in healthy structures.

The approach distinguishes damage from normal variation.

The method provides a probabilistic framework for structural health monitoring.

Abstract

Reductions in natural frequency are often used as a damage indicator for structural health monitoring (SHM) purposes. However, fluctuations in operational and environmental conditions, changes in boundary conditions, and slight differences among nominally-identical structures can also affect stiffness, producing frequency changes that mimic or mask damage. This variability has limited the practical implementation and generalisation of SHM technologies. The aim of this work is to investigate the effects of normal variation, and to identify methods that account for the resulting uncertainty. This work considers vibration data collected from a set of four healthy full-scale composite helicopter blades. The blades were nominally-identical but distinct, and slight differences in material properties and geometry among the blades caused significant variability in the frequency response functions, which presented as four separate trajectories across the input space. In this paper, an overlapping mixture of Gaussian processes (OMGP), was used to generate labels and quantify the uncertainty of normal-condition frequency response data from the helicopter blades. Using a population-based approach, the OMGP model provided a generic representation, called a form, to characterise the normal condition of the blades. Additional simulated data were then compared against the form and evaluated for damage using a marginal-likelihood novelty index.