Assessment and reduction of uncertainties in operational modal analysis (RUN-OMA)

• Applicant: Professor Dr.-Ing. Volkmar Zabel
• Subject Area: Applied Mechanics, Statics and Dynamics
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 459059742

The experimental identification of the vibrational characteristics of mechanical structures by modal parameters under ambient excitation is the aim of Operational Modal Analysis. For this purpose, a number of reliable and well-proven methods based on formulations both in frequency and time domain are available offering various advantages and disadvantages. However, due to the stochastic nature of measured vibration signals and complex relations between a vibrating structure, measurement process and analyses, the modal parameters identified by these methods are often subject to significant uncertainties. These can causally be attributed to random effects (aleatory uncertainty) or incomplete knowledge (epistemic uncertainty), as well as to combinations of both (polymorphic uncertainty). The aim of this research proposal is to develop a methodology for the identification and quantification of polymorphic uncertainties in the output quantities of consecutive steps in the measurement and analysis process, starting from measured vibration signals to identified systems and modal parameters. This includes the development of evaluation criteria for these output quantities. Another aim is the determination of the interaction and the quantification of influences of multiple factors on the uncertainty of identified modal parameters, as well as their reduction by optimization of the governing factors. The development of methods is based on a parametric model of the measurement and analysis process taking polymorphic uncertainties into account. Primarily, synthetic vibration signals will be used in this project. Within this context, two well-established parametric methods of Operational Modal Analysis are considered. Moreover, methods from signal theory, machine learning, polymorphic uncertainty modelling, uncertainty quantification (Monte Carlo methods and optimization procedures) and sensitivity analysis are applied. By means of the proposed methodology, decisions in context with further investigations based on modal parameters identified by means of an Operational Modal Analysis are supported. In every step of the process, from measurement to the identification of modal parameters, intermediate results can be evaluated and optimized on the basis of objective criteria to achieve results with minimal uncertainties. The methodology is designed in such a way that it can basically be transferred to real measurement and analysis processes.

• DFG Programme: Research Grants