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).

Abstract

'Civil structures, once built and released for use, are expected to fulfil operational standards for extended periods. The fact, a civil structure´s life, is determined by temporary deterioration, harsh ambient, misuse, destruction, and, eventual rehabilitation for current use or conservation for historical reasons. Initially, vibrational analysis with stationary sensors, broad logistics and a large array of sensors to provide densely detailed data were required. The next evolutionary step; the simplification of the while-under-use data-acquisition stage; better, faster, more detailed parameters for diagnosis. With the structure under normal operation, a stationary reference sensor, a mobile sensor able to travel through specific trajectories over the structure, deep computational data analysis provides better, deeper, clearer frequencies, mode shapes and damping ratios. A structural health monitoring system should be able to reliably assess structural wellness under operational conditions. Such are the benefits of a mobile sensor monitoring and diagnosis system. This paper utilizes the time–scale analysis method and the Continuous Wavelet Transform (CWT) through a complex Morlet wavelet to extract modal parameters from the signal responses. The theoretical development is validated by numeric simulations on a simply supported beam and by double experimental confirmation on a pedestrian bridge at Universidad del Valle, in Cali (Colombia), by the technique herein, and by a stationary sensor scheme.