The most common type of modal testing system today uses an FFT analyzer to measure a set of Frequency Response Functions (FRFs) from a structure, and then uses a parameter estimation (curve fitting) method to determine the structure’s modal properties from the FRF measurements. The curve fitting method typically “fits” an analytical model to the FRF data, (or its equivalent Impulse Response dat...

This paper investigates a new representation format for dynamic discourse in DRT, where contextual dynamics is modeled in terms of update conditions. This new representation format is motivated by the study of context dependence in modal constructions, in particular by serious problems besetting earlier approaches to modality and modal subordination in DRT. We present an alternative DRT analysi...

Modal parameter extraction of high speed shafts is of critical importance in mechanical design of turbo-pumps. Due to the complex geometry and peripheral components of turbo-pumps, difficulties can arise in determination of modal parameters. In this study, modal properties of a turbo-pump shaft, was studied by experimental modal analysis, and using different excitation techniques. An innovative...

This paper presents a comparative study of conventional Experimental Modal Analysis (EMA) and output-only Operational Modal Analysis (OMA) techniques for estimating the modal parameters of a moderately damped truck chassis. The OMA, without its need to measure input forces, would serve as a convenient methodology to test vehicle structures when response data alone is available. Data has been ac...

This paper introduces a perturbative inversion algorithm for determining sea floor acoustic properties, which uses modal amplitudes as input data. Perturbative inverse methods have been used in the past to estimate bottom acoustic properties in sediments, but up to this point these methods have used only the modal eigenvalues as input data. As with previous perturbative inversion methods, the o...

In multi-modal learning, data consists of multiple modalities, which need to be represented jointly to capture the real-world ’concept’ that the data corresponds to (Srivastava & Salakhutdinov, 2012). However, it is not easy to obtain the joint representations reflecting the structure of multi-modal data with machine learning algorithms, especially with conventional neural networks. This is bec...

A framework for the probabilistic finite element model updating based on measured modal data is presented. The described applied to a seven-storey building made of cross-laminated timber panels. experimental estimates forced vibration test are used in process updating. First, generalized Polynomial Chaos surrogate derived representing map from parameters eigenfrequencies and eigenvectors. To ov...

In this article, we consider multi-modal circular data and nonparametric inference. We introduce a doubly flexible method based on Dirichlet process mixtures in which parameter assumptions are relaxed. assess discuss simulation studies the efficiency of proposed extension relative to standard finite mixture applications analysis data. The real application shows that relaxed approach is promisin...