Structural Health Monitoring for Rotating Machinery based on Operational Modal Analysis and Artificial Intelligence

Modern aircraft turbofan engines are exposed to high temperatures, combined loads, environmental influences and require great manufacturing effort featuring state-of-the-art materials with additional treatment technology like coating and shot peening. As engine technology evolves, requirements to data acquired by mechanical testing facilities rise as well, demanding increased precision and comprehensive determination of structural response at various loading scenarios.
The proposed research aims to develop a novel hybrid method for real-time evaluation of the structural condition in the context of mechanical spinning tests. The main prospect of this research is to contribute to increased efficiency and informative value of conducted mechanical tests. This poses beneficial implications for further areas relying on test data, including the optimisation of component design, potential reduction of maintenance costs and increased operational safety of gas turbine engines. The presented aim of the project can be subdivided into the three following key objectives, which will be covered in the course of this research.
The first objective is to establish a method, which allows to accurately estimate the dynamic (modal) properties of a rotating structure during test operation using Operational Modal Analysis (OMA). Existing methods do not sufficiently cover various aspects, which are specific to spinning tests, e.g. the high degree of harmonic loading with low amplitude random excitation, changing operating speed, varying temperatures due to friction in bearings and gears, etc. Therefore, the presented research will evaluate and address current limitations in this area.
Measured modal parameters of the tested system can be used to adjust corresponding structural computer models, which are usually based on Finite Elements (FE), to achieve a closer representation of the real part or assembly. This process, known as model updating, is especially desirable since FE models of mechanical structures have become a key element in design processes with the rise of Computer Aided Engineering (CAE). Since model updating involves iterative simulations, computing duration becomes a limiting factor. Therefore, a further objective is to investigate the implementation of model updating for rotating structures in conjunction with novel optimisation algorithms.
Finally, the third objective is to cover the integration of Artificial Intelligence (AI) into processes for structural health monitoring of a tested system, e.g. a rotating fan, since current research in this area is mostly limited to civil engineering applications. Two main approaches are considered for this purpose. On one hand, an adaptable characterisation of the nominal (undamaged) condition, utilising machine learning based on actual measurement data, has the potential to increase the reliability of fault detection. On the other hand, AI-driven models can be trained on FE simulation data of a mechanical system in different structural conditions. When applied to actual test runs afterwards, such AI models may allow to characterise and locate damage in real-time, without the impeding computational demands of the original FE simulations.