Automatic System Identification Algorithm Including Uncertainty Estimation based on Operational Modal Analysis

Shakeri, Mohammad Sajad | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52005 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Khodaygan, Saeed; Movahedi, Mohammad Reza
  7. Abstract:
  8. Modal analysis is very important in determining the physical properties of structures and mechanisms. This field is divided into two general parts: Experimental Modal Analysis (EMA) and Operational Modal Analysis (OMA). In the operational modal analysis, there are more challenges for the analyst due to the lack of inputs to identify the system. Many of the mechanical structures can not be analyzed experimentally and under specific loadings, but should be identified under actual operating conditions and in situations where the inputs of the system are not measurable. For this purpose, a modal analysis method called "Random Sub-Space Identification" (SSI) is intoduced, which uses only output data to identify system parameters. An important point in the identification process in systems with an unknown input is to specify the system's order, which is completely unknown. Therefore, the analysis is performed for systems with orders much higher than the actual order of the system. This will lead to the detection of spourios and computational modes. Therefore, to eliminate these modes, it is necessary to analyze a diagram called a stabilization diagram. Interpretation of the stabilization diagram is usually done by the modal analyst. On the other hand, due to the random nature of the SSI, we will have uncertainty in the estimated modal parameters, which can be an effective measure in the investigation of the stabilization diagram and then the finding of the system's final modal parameters. The purpose of this study is to provide an algorithm for automatic identification of modal characteristics of a system based on stochastic subspace in the field of operational modal analysis, using the uncertainty criterion in clearing the stabilization diagram and its automatic interpretation. In the first example, the results are compared with the exact values and the most effective parameters on the estimation error and the amount of uncertainty is determined as the damping ratio. In the second example, using real data, the results of the algorithm are compared with the results of another valid method
  9. Keywords:
  10. Operational Modal Analysis ; Modal Parameters ; System Identification ; Stochastic Subpace Identification ; Stabilization Diagram ; Automatic Interpretation ; Uncertainty Bounds ; Structural Health Monitoring

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