Towards an automatic spectral and modal identification from operational modal analysis

A method is developed for the automatic identification of the spectrum and modal parameters of an operational modal analysis using multi sensors. A multivariate autoregressive model is presented, and its parameters are estimated by least squares via the implementation of QR factorization. A noise-independent minimum model order, from which all available physical modes may be identified, is developed. This so-called optimal model order is selected from the convergence of a global order-wise signal-to-noise ratio index. At this model order or higher, the modes are classified based on a decreasing damped modal signal-to-noise (DMSN) criterion. This decreasing order classification allows for easy identification of all the physical modes. A significant change in the DMSN index enables the determination of the number of physical modes in a specific frequency range, and thus, an automatic procedure for identifying the modal parameters can be developed to discriminate harmonic and natural frequencies from spurious ones. Furthermore, a multispectral matrix can be constructed from selected frequencies by introducing a powered amplification factor, which provides a smooth, balanced, noise-free spectrum with all main peaks. The proposed method has been performed on simulated multi-degree-of-freedom systems, on a laboratory test bench, and on an industrial operating high power hydro-electric generator offering the potential for automatic operational modal analysis and structural health monitoring.

► A method for computing of noise-free spectrum from multi-output operating modal analysis.
► A global Noise-rate Order Factor for selecting autoregressive model order.
► A damped modal signal-to-noise criterion for classifying eigenmodes.