Monday  |  Pecos  |  09:40 AM–10:00 AM

#14981–Automated Modal Analysis Through Machine Learning: An Industrial Validation

Modal analysis has developed into a major technology for the study of structural dynamics, in the past several decades. Through it, complex structural dynamics phenomena can be represented in terms of structural invariants, i.e., the modal parameters: natural frequencies, damping ratios and mode shapes. These analyses are often performed by expert analysts who manually select the system’s poles (which represent the modal parameters), from the so-called stabilization diagrams. Furthermore, the difficulty of interpreting stabilization diagrams increases with the complexity of the dataset, sometimes requiring an expert with a high level of domain knowledge to interpret it. Therefore, the automation of modal analysis is important to accurately process complex datasets without user-dependent interaction and with repeatability. In this work, an automated modal analysis (AMA) algorithm is proposed using a Machine Learning (ML) clustering technique, combined with domain knowledge of modal parameter selection. The algorithm is benchmarked against the manual selection of multiple engineers with different levels of modal analysis expertise. The benchmark study consists of the analysis of a complex fighter jet dataset. A comparative overview of the results is described in this paper, along with the advantages of the AMA algorithm.

André Tavares   KU Leuven/Siemens Industry Software NV
Emilio Di Lorenzo   Siemens Industry Software NV
Bram Cornelis   Siemens Industry Software NV
Simone Manzato   Siemens Industry Software NV
Bart Peeters   Siemens Industry Software NV
Wim Desmet   KU Leuven
Konstantinos Gryllias   KU Leuven