Signal Processing Techniques for Nonlinear Structural Dynamical Systems

1991 ◽  
Vol 44 (11S) ◽  
pp. S214-S218 ◽  
Author(s):  
C. Pezeshki ◽  
W. H. Miles ◽  
S. Elgar
Keyword(s):  
Signal Processing ◽  
Steady State ◽  
Wavelet Transforms ◽  
Galerkin Approximation ◽  
Higher Order ◽  
Steady State Response ◽  
State Response ◽  
Higher Order Spectra ◽  
Processing Techniques ◽  
Signal Processing Techniques

Various signal processing techniques are introduced into the structural dynamics literature, notably higher-order spectra for steady-state response and wavelet transforms for transient response of systems. The structural behavior of the buckled beam, modeled by the one-mode Galerkin approximation is examined to demonstrate the utility of the techniques. Higher-order spectra illuminate nonlinear energy coupling mechanisms in the frequency domain for the steady state response. Wavelet transforms show the development of the frequency spectrum in the transient portion of the response.

Effect of Axial Load on the Response of Beams on Nonlinear Viscoelastic Unilateral Foundations

2014 ◽  
Author(s):  
Udbhau Bhattiprolu ◽  
Anil K. Bajaj ◽  
Patricia Davies
Keyword(s):  
Steady State ◽  
Axial Load ◽  
Galerkin Approximation ◽  
Computation Time ◽  
Polyurethane Foams ◽  
Steady State Response ◽  
Approximation Solution ◽  
Solution Approach ◽  
State Response ◽  
Nonlinear Viscoelastic

Flexible polyurethane foams used for cushioning in the furniture and automotive industries serve as foundations and exhibit complex nonlinear viscoelastic behavior. To design systems that incorporate these materials, it is important to model their mechanical behavior and then to predict the dynamic response of such systems. The example of a pinned-pinned beam interacting with a nonlinear viscoelastic foundation is the focus of the present study. The foundation can either react in compression as well as tension (bilateral), or react only in compression (unilateral). In the latter case, the contact regions between the beam and the foundation are not known a priori, and thus the coefficients of the modal equations obtained in a Galerkin approximation solution approach, are functions of the solution as well. It is therefore computationally expensive to predict the dynamic and steady-state response of these structures to static and harmonic loads. For polynomial-type nonlinearities, it is possible to speed up the computation time by using a convolution method to evaluate integral terms in the model. Also, if only the steady-state response is of interest, direct-time integration can be replaced by incremental harmonic balance to make the frequency response predictions more efficient. The effect of axial load and the influence of various parameters e.g., loading configuration, excitation amplitude, linear and nonlinear stiffness, on the response of the beam on unilateral and bilateral foundations are studied.

scholarly journals Power Quality Detection, Classification and Monitoring Using LABVIEW

2019 ◽  
Vol 4 (2) ◽  
pp. 101-111
Author(s):  
Fatma Zohra DEKHANDJI ◽  
Salim TALHAOUI ◽  
Youcef ARKAB
Keyword(s):  
Signal Processing ◽  
Power Quality ◽  
Wavelet Transforms ◽  
Support Vector ◽  
Labview Software ◽  
Quality Detection ◽  
Power Quality Disturbances ◽  
Processing Techniques ◽  
Signal Processing Techniques

In recent years, Power Quality becomes increasingly a major concern for both electric utilities and end users. Accordingly, the electrical engineering community has to deal with the analysis, diagnosis and solution of PQ issues using system approach rather than handling these issues as individual problems. This paper describes the analysis of PQ using advanced signal processing tools represented in Hilbert & Wavelet Transforms (HT-WT) and artificial intelligence tools represented in Artificial Neural Network & Support Vector Machine (ANN-SVM) for detection and classification of power quality disturbances respectively. These techniques were successfully simulated using LABVIEW software capabilities. The results of simulation indicate that the signal processing techniques are effective mechanisms to detect and classify power quality disturbances. At the end, the combination of WT as a tool of detection and features extraction with SVM as a classifier tool resulted as the best combination for PQ monitoring system.

A Comparative Study of Power Quality Monitoring Using Various Techniques

2021 ◽  
pp. 259-288
Author(s):  
Fatma Zohra Dekhandji ◽  
Mohamed Cherif Rais
Keyword(s):  
Signal Processing ◽  
Power Quality ◽  
Wavelet Transforms ◽  
Support Vector ◽  
Labview Software ◽  
Power Quality Monitoring ◽  
Power Quality Disturbances ◽  
Processing Techniques ◽  
Signal Processing Techniques

In recent years, power quality (PQ) has become an increasingly major concern for both electric utilities and the end users. Accordingly, the electrical engineering community has to deal with the analysis, diagnosis, and solution of PQ issues using system approach rather than handling these issues as individual problems. This chapter describes the analysis of PQ using advanced signal processing tools represented in Hilbert and wavelet transforms (HT-WT) and artificial intelligence tools represented in artificial neural network and support vector machine (ANN-SVM) for detection and classification of power quality disturbances, respectively. These techniques were successfully simulated using LABVIEW software capabilities. The results of simulation indicate that the signal processing techniques are effective mechanisms to detect and classify power quality disturbances. At the end, the combination of WT as a tool of detection and features extraction with SVM as a classifier tool resulted as the best combination for PQ monitoring system.

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