Abstract

Operational Modal Analysis (OMA) is a common tool for identification of dynamic parameters of structures during operation using output-only data. Modal shapes are traditionally identified in a static setting where stationary sensors are fixed at locations and contain profitable structural responses, however, data contains restricted spatial information. Mobile sensors can provide extensive data information like a dense stationary sensors array. Mobile sensing provides several advantages over static schemes using stationary sensors, but the main advantage is a single mobile sensor can be used to record signals continuously along the structure. Signals obtained by mobile sensors during dynamic events (e.g. ambient vibrations) contain nonlinear, nonstationary, and noisy properties, thus, it has significant variations in its spectral content over time, requiring a suitable processing to extract the properties in both time and frequency domains. A wavelet-based method is proposed to perform modal identification for output-only systems identified using mobile sensors. A Morlet wavelet is used, as it can decouple the measured multicomponent signal to monocomponent signals in the form of complex-valued, and then the identification scheme for single-degree-of freedom systems can be implemented to extract the modal parameters. In this paper, it is shown how the amplitude and the phase of the wavelet transform of recorded signals from the mobile sensor are related to eigenfrequencies and damping coefficients. Modal shapes are identified using transmissibility functions. Also, the effect of noise on the extracted modal parameters is investigated. The validity of the method is demonstrated by a numerical case study using theoretical results and Smoothed Pseudo-Wigner-Ville Distribution (SPWVD).