Noise rejection for two time-based multi-output modal analysis techniques

Modal analysis is a well-developed field with many applications. In particular, forced response multi-output approaches are well suited for system identification and online damage detection because they use the natural excitations the system undergoes during its normal operation. In this work, two of these approaches, smooth orthogonal decomposition (SOD) and direct system parameter identification (DSPI), are analyzed, compared, and improved upon. SOD was originally developed as a tool for detecting features of chaotic dynamical systems. Recently, it has been used as a time-based multi-output modal analysis approach. SOD has been demonstrated to work for the free vibration case and for random excitations. DSPI was developed as a time-based multi-input multi-output approach. When the inputs are not measured, DSPI is very similar to SOD and can handle both free vibrations and random excitations. However, if the inputs are measured or known DSPI can also handle arbitrary excitations. To improve the performance of these two methods when used with noisy data, novel noise filtering algorithms are proposed. Numerical simulations are performed to compare the two methods and to show the effectiveness of the filtering algorithms in improving frequency and mode shape extraction.