Operational Modal Analysis of Aeronautical Structures via Tangential Interpolation

TL;DR

This paper introduces NExT-LF, a novel frequency-domain operational modal analysis method combining the Loewner Framework with NExT-ERA, improving efficiency and noise robustness for aeronautical structures.

Contribution

It proposes a new OMA technique that couples the Loewner Framework with NExT-ERA, enhancing computational efficiency and noise resilience in modal parameter identification.

Findings

NExT-LF accurately identifies modal parameters on experimental aeronautical benchmarks.

The method shows good correlation with reference data, especially at low frequencies.

NExT-LF outperforms traditional SSI and NExT-ERA in noisy conditions.

Abstract

Over the last decades, progress in modal analysis has enabled increasingly routine use of modal parameters for applications such as structural health monitoring and finite element model updating. For output-only identification, or Operational Modal Analysis (OMA), widely adopted approaches include Stochastic Subspace Identification (SSI) methods and the Natural Excitation Technique combined with the Eigensystem Realization Algorithm (NExT-ERA). Nevertheless, SSI-based techniques may become cumbersome on large systems, while NExT-ERA fitting can struggle when measurements are contaminated by noise. To alleviate these, this work investigates an OMA frequency-domain formulation for aeronautical structures by coupling the Loewner Framework (LF) with NExT, yielding the proposed NExT-LF method. The method exploits the computational efficiency of LF, due to the effectiveness of tangential interpolation, together with the impulse response function retrieval enabled by NExT. NExT-LF is assessed on two experimental benchmarks: the eXperimental BeaRDS 2 high-aspect-ratio wing main spar and an Airbus Helicopters H135 bearingless main rotor blade. The identified modal parameters are compared against available experimental references and results obtained via SSI with Canonical Variate Analysis and NExT-ERA. The results show that the modes identified by NExT-LF correlate well with benchmark data, particularly for high-amplitude tests and in the low-frequency range.