A decentralized blind source separation algorithm for ambient modal identification in the presence of narrowband disturbances.

ABSTRACT In this paper, a new decentralized identification method based on the principles of blind source separation (BSS) that can perform modal identification in the presence of narrowband external excitations is presented. This algorithm is based on the principle of sparse BSS involving wavelet transforms to undertake identification for underdetermined static mixtures. Most BSS methods in the context of modal identification assume that the excitation is white and does not contain narrow band excitation frequencies. However, this assumption is not satisfied in many situations when the excitation is a superposition of narrowband harmonic(s) and broadband disturbance, for example, rotating machinery fixed to flexible structures, human-induced vibration in pedestrian bridges, or dynamic interaction with surrounding structures. Under such conditions, traditional BSS methods (such as second-order blind identification) yield sources (modes) without any indication as to whether the identified source(s) is a system or an excitation harmonic. In the proposed method, underdetermined BSS is employed, involving transformation of measurements to the time-frequency domain, resulting in a sparse representation. The statistical characteristics of the sources are estimated using residual bootstrap technique, which are then used to delineate the sources corresponding to external disturbances versus intrinsic modes of the system, in a decentralized fashion. The decentralized architecture is aimed at reducing the cost of instrumentation in both traditional wired and recent wireless applications. The performance of the algorithm is demonstrated using both a numerical study and a bench-scale laboratory experiment. The primary advantage of the proposed method is its ability to identify the modal harmonics of a flexible structure under narrowband excitations, in a decentralized fashion, where traditional BSS algorithms fail to achieve the same goals. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]