# A Tensor-based Structural Health Monitoring Approach for Aeroservoelastic Systems

## TL;DR

This paper introduces a tensor-based method for structural health monitoring in aerospace, effectively analyzing complex sensor data to detect damage in aeroservoelastic systems.

## Contribution

It presents a novel tensor analysis approach combined with a Lagrangian aeroservoelastic model for improved damage detection in aerospace structures.

## Key findings

- Tensor analysis effectively handles redundant sensor data.
- The method improves damage detection accuracy.
- Application to a complex aeroservoelastic model demonstrates effectiveness.

## Abstract

Structural health monitoring is a condition-based field of study utilised to monitor infrastructure, via sensing systems. It is therefore used in the field of aerospace engineering to assist in monitoring the health of aerospace structures. A difficulty however is that in structural health monitoring the data input is usually from sensor arrays, which results in data which are highly redundant and correlated, an area in which traditional two-way matrix approaches have had difficulty in deconstructing and interpreting. Newer methods involving tensor analysis allow us to analyse this multi-way structural data in a coherent manner. In our approach, we demonstrate the usefulness of tensor-based learning coupled with for damage detection, on a novel $N$-DoF Lagrangian aeroservoelastic model.