Nonstationary dynamics data analysis with wavelet-SVD filtering

F_ g Nonstationary time-frequency analysis is used for identificaj tion and classification of aeroelastic and aeroservoelastic dyi, k, l namics. Time-frequency multiscale wavelet processing genrn, n erates discrete energy density distributions. The distribuP,q tions are processed using the singular value decomposition r, 8, t (SVD). Discrete density functions derived from the SVD genR erate moments that detect the principal features in the data. The SVD standard basis vectors are applied and then cornSRA pared with a transformed-SVD, or TSVD, which reduces the SVD number of features into more compact energy density concenTSVD trations. Finally, from the feature extraction, wavelet-based W a modal parameter estimation is applied. The primary objective is the automation of time-frequency analysis with modal system identifcation. The contribution is a more general approach in which distinct analysis tools are merged into a unified procedure for linear and nonlinear data analysis. This method is first applied to aeroelastic pitchplunge wing section models. Instability is detected in the linear system, and nonlinear dynamics are observed from the time-frequency map and parameter estimates of the nonlinear system. Aeroelastic and aeroservoelastie flight data from the DAST (drone for aerodynamic and structural testing) and F18 aircraft are also investigated and comparisons made between the SVD and TSVD results. Input-output data is used to show that this process is an efficient and reliable tool for automated on-line analysis.