Our R&D project manager, Ahmed Jhinaoui, did his PhD at INRIA (French Institute for Research in Computer Science and Control). The subject of his work is part of a wider research thematic in the context of a collaboration between INRIA and ISAE (French Aerospace Institute), via the project I4S which is about structural health monitoring for the purpose of reducing maintenance interventions.


Client problem

Ground resonance is an instability that may occur when a helicopter is spinning on or near the ground. This phenomenon may lead to violent oscillations causing the tail to touch the ground and the destruction of the aircraft. The first goal of this work was to develop a vibration-based diagnostic system for helicopters’ rotors, which allows early detection of ground resonance phenomenon.

In academic literature, a manifold of works has dealt with the identification of modal properties of mechanical systems, and with online monitoring of these properties in order to guarantee the condition of the structure. Stochastic Subspace Identification is still today widely used in industry. Recently, a new monitoring method based on that subspace approach and on statistical inferences has been suggested and has been applied and validated on many aeronautical systems.

Unfortunately, both of the methods are applicable when a system is considered to be time-invariant. Therefore, the transposition of the obtained results cannot be straightforward for systems like helicopters which are inherently, by their rotating part, time-periodic. The purpose of the study was, thus, to extend the applicability of those methods to the helicopter’s case.

The scope of the study was then expanded to wind turbine monitoring, which exhibits analogous dynamical behavior.

Solution proposed

Starting from the state of the art and in particular the works realized at ISAE, a simplified model of a helicopter was used in order to study the behavior of the ground resonance phenomenon. The equation of motion of this model leads to a differential equation with time-periodic coefficients. The stability of time-periodic systems is given by a condition about some parameters called Floquet multipliers.

An Operational Modal Analysis (OMA) algorithm, using the subspace approach, was developed in order to identify Floquet multipliers from vibratory sensors. After bringing theoretical proofs of this new method, uncertainties related to measurements errors were studied and the algorithm was shown to be efficient, in a mathematical sense.

Using this new Algorithm, an online monitoring algorithm was suggested, for detecting instabilities on periodic systems. This algorithm allows to track the Floaquet multipliers of a system and to trigger an alarm when its behavior is close to unstable regimes. The developed methods were, finally, validated on a helicopter test. The used testbench was designed by ISAE for the study of ground resonance and is the first test in Europe designed for such phenomena. They have, as well, been validated on wind turbine data from the HIT of Harbin, China.

Other missions

  • PhD

Our CTO, Baptiste Coulange, did his PhD for CNES (the French Space Agency) and University of Paris Descartes on detecting of aliasing in satellite images.

Earth observation satellites are optimised to obtain the best image quality. The size of the sensors and the parameters of the optical chain are designed to obtain the best compromise between image resolution and acquisition artefacts. The higher the resolution the more artefacts are present.

Aliasing which is one of the artefacts in satellite images can lead to image misinterpretation. It is thus critical to detect it. By using the duality between spatial localisation in the image and aliasing relationships in the Fourier transform plane, it was possible to develop and validate a aliasing detection algorithm.

Thibault Gouache (cofounder of Cornis), did his PhD for ESA (European Space Agency), in collaboration with ISAE (Toulouse, France) and the University of Surrey (Guilford, UK) in the field of automatic and mechanics. A strong expertise in design, assessment, evaluation and testing of space mechanisms was acquired.

Our research project manager, Virginie Delavaud, carried out her PhD thesis for SNCF, the french railway company, with ENSTA ParisTech, about railway rolling noise.

Rolling noise is the main source of railway transportation noise for a wide speed range (between 50 and 320 km/h). Rolling noise occurs when a wheel moves on a rail in a straight line. On the same scope, the impact noise is due to discrete irregularities on either of the two structures, such as rail joints or wheelflats.