Short Data-Based Output-Only Identification for Time-Varying Systems with Fast Dynamic Evolution

Abstract EN

Many engineering systems change appreciably over a relatively short time interval due to their fast evolution in the dynamics.

Time-varying (TV) system’s ambient excitation is usually difficult to measure under operating conditions, and its dynamics have to be determined without measuring the excitation.

Therefore, short data-based output-only identification for TV systems with fast dynamic evolution is considered in this paper.

Deterministic parameter evolution methods are known to track fast dynamic evolution by postulating TV model parameters as deterministic functions of time and selecting proper functional subspaces.

However, these methods require a significant number of parameters to represent complicated time-dependencies and dynamics characterized by larger numbers of degrees-of-freedom.

In such cases, the ordinary least squares estimation may lead to less accurate or even unreliable estimates.

A ridge regression-based deterministic parameter evolution method is proposed to overcome ill-posed problems via regularization and subsequently assessed through numerical and experimental validation.

Comparative results confirm the advantages of the proposed method in terms of achievable natural frequency and power spectral density tracking, accuracy, and resolution of TV systems with fast dynamic evolution, when the response data length is relatively short.