| description abstract | The safety of integrally bladed rotors is often assessed through bench-level vibration tests to measure amplification factors and back-out sector frequency deviations via mistuning identification (ID) algorithms. This process is usually composed of two separate steps. First, a system ID step is completed to identify system modal data. Then, these data are input into mistuning ID algorithms. Errors in identified modal data will then propagate to produce errors in the system's predicted mistuning. Obtaining robust mistuning estimates then requires larger quantities of accurate modal data. This effort seeks to attain accurate mistuning data by coupling the system and mistuning ID steps into a more parallel, versus serial, process that is capable of identifying many system modes. An iterative polyreference-least squares complex frequency-domain (P-LSCF) algorithm finds modal data, and a mistuning ID algorithm obtains mistuning data at each iteration. An outlier detection method is proposed to remove spurious modes that cause erroneous mistuning results. Then, a weighted, least-squares regression approach is employed to remove the impact of sector-specific outlier data. This method reduces errors in identified mistuning parameters from the fundamental mistuning model ID algorithm. Furthermore, the approaches eliminate the need for users to determine true versus spurious system modes in each iteration of the P-LSCF algorithm, thus removing ambiguity. The developed approaches are tested on simulated and bench-top test data. Results show the efficacy of the developed approaches and their ability to account for uncertainty in mistuning parameters. | |
