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

利用环境激励下的模态参数对机械结构的振动特性进行实验识别是操作模态分析的目的。为此目的，许多可靠的和经过充分验证的方法的基础上制定在频域和时域提供各种优点和缺点。然而，由于测量的振动信号的随机性和振动结构，测量过程和分析之间的复杂关系，这些方法识别的模态参数往往受到显着的不确定性。这些可以归因于随机效应（偶然的不确定性）或不完整的知识（认知不确定性），以及两者的组合（多态不确定性）。本研究建议的目的是开发一种方法，用于识别和量化测量和分析过程中连续步骤的输出量中的多态不确定性，从测量的振动信号到识别的系统和模态参数。这包括为这些产出量制定评价标准。另一个目的是确定的相互作用和量化的影响，多个因素的不确定性的识别模态参数，以及他们的减少，通过优化的管理因素。方法的发展是基于一个参数模型的测量和分析过程中考虑到多态性的不确定性。本项目将首先采用合成振动信号。在这方面，两个完善的参数化方法的操作模态分析被认为是。此外，信号理论，机器学习，多态不确定性建模，不确定性量化（蒙特卡洛方法和优化程序）和灵敏度分析的方法被应用。通过所提出的方法，决策的背景下，进一步调查的基础上确定的模态参数，通过操作模态分析的支持。在从测量到模态参数识别的过程的每一步中，都可以根据客观标准评估和优化中间结果，以获得具有最小不确定性的结果。该方法的设计方式，它基本上可以转移到真实的测量和分析过程。

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.