Robust Operational Modal Analysis of nonlinear and nonstationary systems

Tobias Friis

Research output: Book/ReportPh.D. thesisResearch

68 Downloads (Pure)

Abstract

Large structures and mechanical systems, such as offshore structures, power plants, bridges and airplanes, have a critical impact on a civilisation’s economy and standard of living; therefore, their structural integrity is of crucial importance. Traditional approaches to examine the structural integrity rely on manual inspection, which is both time-consuming and costly due to the manual labour, complexity of the inspection and complicated accessibility. The computerbased framework of Structural Health Monitoring has been developed in recent decades to reduce manual inspection demands, avoid downtime and extend the lifetime of these structures. This leads to both reduced costs for maintenance and renewal, a prolonged period of profit and increased sustainability.
The principle step of Structural Health Monitoring is the identification of dynamic characteristics from measured vibrations of the investigated structure in the form of modal properties. These modal properties are commonly estimated by the use of Operational Modal Analysis (OMA), and are employed to periodically detect potential structural faults and serve as the foundation for assessing the structural integrity and remaining lifetime. The OMA techniques, however, are in principle confined to estimation of modal properties of structures that can be assumed to behave linear and stationary (i.e. time-invariant), which does not always comply with the aforementioned real-life structures.
In this thesis, the application of OMA to measured vibration responses of structures, that behave nonlinear and/or nonstationary (i.e. time-varying), is investigated in relation to subsequent use in Structural Health Monitoring. The work of the thesis addresses the state of the art of OMA with the focus on methods that employ correlation functions. It is proven and demonstrated that these methods can be employed to obtain reliable and appropriate modal properties from responses of nonlinear and/or nonstationary structures. In addition, the derivation of the associated minimisation scheme of the difference between these approximating modal properties and the true structure is provided. An appropriate simulation study reveals difficulties when assessing the so-called goodness-of-fit of this approximation, which should be considered by the practitioner in the further use of the obtained modal properties. Moreover, an approach based on the Random Decrement technique is proposed to evaluate a type of complicated nonlinear structures by two or more sets of modal properties. It is demonstrated how the proposed approach enables Structural Health Monitoring of complex structures such as bridge-connected offshore platforms. Furthermore, issues arising in both the OMA-based identification and the associated Structural Health Monitoring framework related to structures with oscillating masses, i.e. fluid tanks and vibration absorbers, are identified and a strategy to overcome said issues is proposed. The possibility to estimate the parameters of nonlinear and/or time-varying models of the aforementioned structures in operational conditions is discussed together with the future In conclusion, the PhD project provides essential advancements of the principle step in Structural Health Monitoring that, in turn, enables its application to a larger group of structures outside of the laboratory environment. work related to the outcome of the project.
In conclusion, the PhD project provides essential advancements of the principle step in Structural Health Monitoring that, in turn, enables its application to a larger group of structures outside of the laboratory environment.
Original languageEnglish
PublisherTechnical University of Denmark, Department of Civil Engineering
Number of pages174
ISBN (Electronic)87-7877-528-8
Publication statusPublished - 2020

Fingerprint

Dive into the research topics of 'Robust Operational Modal Analysis of nonlinear and nonstationary systems'. Together they form a unique fingerprint.

Cite this