A global, single-input–multi-output (SIMO) implementation of the algorithm of mode isolation and application to analytical and experimental data

This work presents a global, single-input–multi-output (SIMO) extension of the algorithm of mode isolation (AMI) in which the frequency response functions (FRFs) from all measurement points are considered simultaneously, resulting in global estimates for natural frequencies and damping ratios, and consistent mode vectors. This allows a large number of response points to be processed quickly and with minimal user interaction and makes determining the number of modes active in a frequency band easier than with a SISO algorithm. To achieve a global, SIMO AMI algorithm, a fast new linear-least-squares, frequency domain, global, SIMO (or common denominator MIMO), multi-degree of freedom (MDOF) fitting algorithm, similar to the LSCF algorithm is derived and implemented in AMI. This implementation of AMI is then compared with the stochastic subspace identification algorithm (SSI) on noise contaminated analytical data. Some practical issues in applying the SSI algorithm are discussed. Monte Carlo simulations are used to compare the natural frequencies and damping ratios found by AMI and SSI for noise contaminated, synthetic data for a frame structure. The results for the analytical structure also illustrate the difficulty in processing data from a structure having modes with a large range of decay rates. Finally, application to data from the Z24 highway bridge in Switzerland demonstrates the speed and flexibility of the algorithm, and validates its performance on experimental data.