Statistical damage localization with stochastic load vectors using multiple mode sets

A Bayesian Probabilistic Damage Detection Using Load-dependent Ritz Vectors

This paper demonstrates the possibility of incorporating load-dependent Ritz vectors, as an alternative to modal parameters, into a Bayesian probabilistic framework for detecting damages in a structure. Recent research has shown that it is possible to extract load-dependent Ritz vectors from vibration tests [2]. This paper shows that load-dependent Ritz vectors have the following potential adva...

Stochastic Greedy Algorithms For Multiple Measurement Vectors

Sparse representation of a single measurement vector (SMV) has been explored in a variety of compressive sensing applications. Recently, SMV models have been extended to solve multiple measurement vectors (MMV) problems, where the underlying signal is assumed to have joint sparse structures. To circumvent the NP-hardness of the `0 minimization problem, many deterministic MMV algorithms solve th...

Damage Localization of Deterministic-Stochastic Systems

A scheme integrated with deterministic–stochastic subspace system identification and the method of damage localization vector is proposed in this study for damage detection of structures based on seismic response data. A series of shaking table tests using a five-storey steel frame has been conducted in National Center for Research on Earthquake Engineering (NCREE), Taiwan. Damage condition is ...

Distributed Localization Algorithm Based on Statistical Uncorrelated Vectors

We have developed a new localization algorithm based on a set of uncorrelated discriminant vectors (SUV). Comparing to the centralized multidimensional localization algorithm MDS-MAP that has been widely used in wireless sensor networks, this algorithm can improve the localization accuracy and reduce the computing complexity. In this algorithm, the solving equation of the double centered matrix...

Damage detection in frame structures using damage locating vectors