A Wavelet-Based Approach to FRF Identification From Incomplete Data

Frequency response function (FRF) estimation from measured data is an essential step in the design, control, and analysis of complex dynamical systems, including thermal and motion systems. Especially for systems that require long measurement time, missing samples in the data record, e.g., due to measurement interruptions, often occur. The aim of this article is to achieve accurate identification of nonparametric FRF models of periodically excited systems from noisy output measurements with missing samples. An identification framework is established that exploits a wavelet-based transform to separate the effect of the missing samples in the time domain from the system characteristics in tre frequency domain. The framework encompasses both a time-invariant and a time-varying wavelet-based estimator, which provides different mechanisms to address the missing samples. Experimental results from a thermodynamical system confirm that the estimators enable accurate identification.