The MLMM modal parameter estimation method: A new feature to maximize modal model robustness. (1st April 2019) - El-Kafafy, Mahmoud; Peeters, Bart; Geluk, Theo; Guillaume, Patrick
Highlights: A new tool added to the MLMM modal parameter estimation method is introduced. This new tool automatically rejects the nonphysical modes from the mode set. The physicalness of a mode is judged by examining its mass sensitivity. The new tool cleans the mode set before starting the validation phase. With the new MLMM tool, more robust modal models are obtained. Abstract: In this paper, a new feature added to the MLMM modal parameter estimation method (Maximum Likelihood estimation of a Modal Model) will be introduced. The MLMM method tackles some of the remaining challenges in modal analysis, e.g. modal analysis of highly-damped cases where a large amount of excitation locations is needed such as the modal analysis of a trimmed car body. The MLMM modal parameter estimator uses the Levenberg-Marquardt optimization scheme to directly fit the modal model to the measured FRFs. The big advantage of the MLMM method over the existing methods is its capability to consider during the estimation phase that the estimated modal model will fulfill some important modal properties (Reciprocity, realness of the mode shapes, negative mass sensitivity of the modes, etc) that are traditionally used to check the physicalness of the estimated modal model in the validation phase afterwards. Therefore, the validation phase is partially combined with the estimation phase in one step. This merging of the estimation and validation steps will help to reduce the time needed for the last two steps of the modal analysis process (i.e., the estimation step & the validation step) and to make the modal parameter estimation step more objective process rather than subjective one. The MLMM method fully integrates, within the estimated modal model, some important physical constraints, which are required for the intended applications, e. g. realness of the mode shape and FRFs reciprocity. More classical modal parameter estimation methods have rarely the possibility to fully integrate these constraints and the obtained modal parameters are typically altered in a subsequent step to satisfy the desired constraints. It is obvious that this may lead to sub-optimal results. The new feature added to the MLMM method and introduced in this article enables the automatic rejection of the modes that have dubious poles (e.g. mathematical pole that models noise effects) from the mode set. This new feature uses the mass sensitivity (MS) as a criterion to judge the physicalness of the mode. The applicability of MLMM with the new added feature to estimate an accurate constrained modal model will be demonstrated using simulation example and two challenging industrial applications.