Parametric time-domain methods for non-stationary random vibration modelling and analysis  A critical survey and comparison ?
A critical survey and comparison of parametric time-domain methods for non-stationary random vibration modelling and analysis based upon a single vibration signal realization is presented. The considered methods are based upon time-dependent autoregressive moving average (TARMA) representations, and may be classified as unstructured parameter evolution, stochastic parameter evolution, and deterministic parameter evolution. The main methods within each class are presented, and model structure selection is discussed. The methods are compared, via a Monte Carlo study, in terms of achievable model parsimony, prediction accuracy, power spectral density and modal parameter accuracy and tracking, computational simplicity, and ease of use. Comparisons with basic non-parametric methods are also made. The results of the study confirm the advantages and high performance characteristics of parametric methods. They also confirm the increased accuracy and performance characteristics of the deterministic, as well as stochastic, parameter evolution methods over those of their unstructured parameter evolution counterparts.
non-stationary vibration estimation and identification time series analysis vibration analysis time-varying systems modal analysis spectral analysis time-frequency analysis time-dependent arma models