Abstract
Time-domain data-driven stochastic subspace identification operational modal analysis (SSI-OMA) is widely used in civil engineering. In this study, such an algorithm is applied to analyse a machine tool test stand, with excitation provided through pseudo-random excitation of the feed table. SSI-OMA is computationally more efficient than previous implementations, with a lower order of analysis (32 instead of 100) and higher bandwidth. The key parameters that determine the quality of SSI-OMA have been determined. It is shown that the function of reference nodes in measurement can be replaced by a time-alignment algorithm, if the noise level in signals are low. In addition, application of micro-electrical-mechanical (MEMS) accelerometers to carry out SSI-OMA in machine tools is investigated.
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