Modal Tracking Research Articles

Two new fast identification algorithms of time-varying modal parameters based on subspace tracking

The procedure of fast identification algorithms of the time-varying modal parameter based subspace tracking is investigated in this paper.After the pretreatment to the updating input/output data,the problem of fast time-varying modal parameter identification changes to be a subspace tracking problem principally.Adopting the principal subspace tracking algorithms,the fast time-varying modal parameter identification algorithms can be deduced.Replacing PAST by NPI and API,two new versions of time-varying modal parameter identification algorithms are proposed.The performance of the fast modal parameter identification algorithms based on different principal subspace tracking algorithms are compared by applying them on a classical time-varying mechanical system of a moving distributed mass-cantilever beam and a 2-DOF spring-mass model.

Near real-time tracking of dynamic properties for standalone structural health monitoring systems

Automated modal parameter identification of civil engineering structures has been analyzed in a previous paper. An original algorithm, named LEONIDA, working in frequency domain, has been presented and a number of test cases have been discussed in order to point out advantages and drawbacks. It has been demonstrated that LEONIDA represents a promising and reliable tool, in particular for modal testing. Conversely, integration of such a procedure into a fully automated structural health monitoring (SHM) system has shown that it can be used as modal information engine, but length of record durations, amount of computational burden and response time lead to recognize that serious drawbacks and limitations exist for a class of applications, such as continuous monitoring of structures in seismically prone areas. In fact, a fast assessment of relevant structure health conditions in the early post-earthquake phase is becoming of interest in different European areas. In such a context, the statistical treatment of measured dynamic properties could be certainly useful, but it requires the collection of an extensive amount of local and global data in a short time. As a consequence, availability of reliable, robust and fairly fast data processing procedures for modal tracking is fundamental whenever really effective and useful SHM systems are adopted to support civil protection activities during seismic sequences. This applies mainly to strategic structures, whose health conditions must be rapidly assessed after any seismic event, in order to securely manage rescue operations. In the present paper, the main issues related to a fast, robust and reliable modal tracking for emergency management are outlined. Then, an automated modal tracking strategy for SHM applications in earthquake prone regions is described. It is based on the knowledge of the experimental mode shapes and a revised concept of spatial filtering. Results of sample applications of the proposed procedure refer to simulated data and to real measurements collected by a SHM system. The latter are representative of operational conditions and of the transient response due to the ground motion induced by the recent L'Aquila earthquake mainshock. Discussion of results will point out advantages and limitations of the data processing strategy.

Modal tracking in a shallow ocean: An adaptive particle filtering approach.

It is well-known that the shallow ocean is an ever changing, uncertain environment dominated by temperature fluctuations and ambient noise. The need for a processor that can adapt to these environmental changes while simultaneously tracking the evolution of modal functions is necessary for localization, inversion, and enhancement. An approach to this problem is made possible by developing a sequential Bayesian processor capable of providing a joint solution to both the modal function tracking (estimation) and environmental adaptivity problem. The posterior distribution required will contain multiple peaks and space-varying statistics requiring a sequential (nonstationary) Bayesian approach. The final design that evolves is a so-called particle filter. The particle filter is a sequential Markov chain Monte Carlo processor capable of providing reasonable performance for a multi-mode (probability distribution), space-varying problem. The tracking results are applied to synthesized pressure-field data from the actual experimental parameters. The tracking results are compared to that of the modern unscented Kalman tracker solution.

Three-dimensional modal mapping in shallow water using simulated acoustic field data

It is well known that the acoustic field propagated through a shallow- water waveguide is sensitive to the properties of the waveguide and its boundaries. Through adiabatic mode theory, it is understood that the modal eigenfunctions and eigenvalues adapt to the local environment and changing boundary conditions. It has been proposed that modal tracking may be employed in a laterally varying waveguide to infer properties of the normal-mode field and the environment. In support of a three-dimensional modal mapping experiment, synthetic three-dimensional acoustic fields are generated to simulate propagation in a shallow-water region in the vicinity of the East Coast strataform site. Synthetic fields are computed using normal-mode and parabolic equation codes and transformed to the wavenumber domain to obtain estimates of the spatially varying spectral content of the modal field. The resulting modal maps are discussed for several experimental regions. [Work supported by ONR and ARL Penn State Univ.]