On the Problem of Multiple-Excitation Multiple-Response Stochastic Structural Dynamics Identification

1991 ◽  
Author(s):  
Jae-Eung Lee ◽  
Spilios D. Fassois
Keyword(s):  
Structural Dynamics ◽  
Structural Model ◽  
Dispersion Analysis ◽  
Structural Systems ◽  
Multiple Response ◽  
Accurate Analysis ◽  
Vibration Displacement ◽  
Vibration Data ◽  
Stochastic Structural Dynamics ◽  
Dynamics Identification

Abstract In this paper an effective approach for the identification of stochastic structural systems from multiple-excitation multiple-response forced vibration data, is introduced. The proposed approach represents a novel extension of the method of Lee and Fassois (1990b), that enables it to: (a) Operate on either vibration displacement, velocity, or acceleration data records, and, (b) perform accurate analysis of the estimated structural model by using a currently introduced exact and physically meaningful Dispersion Analysis methodology. These new features, combined with its other important properties, make the proposed approach not only capable of overcoming the limitations of current techniques, but, also, a comprehensive procedure for multiple-excitation multiple-response stochastic structural dynamics identification. The excellent performance characteristics of the proposed approach are finally verified via numerical simulations with structural systems characterized by well-separated and closely-spaced modes, as well as data corrupted at various noise-to-signal ratios. Comparisons with the Eigensystem Realization Algorithm (ERA), through which the limitations of deterministic methods are illustrated, are also presented.

Recursive Identification of Vibrating Structures from Noise-Corrupted Observations, Part 1: Identification Approaches

1991 ◽  
Vol 113 (3) ◽  
pp. 354-361 ◽  
Author(s):  
R. Ben Mrad ◽  
S. D. Fassois
Keyword(s):  
Least Squares ◽  
Structural Dynamics ◽  
Continuous Time ◽  
Type Model ◽  
Structural Systems ◽  
Model Structure ◽  
Continuous Transformation ◽  
Excitation Signal ◽  
Signal Type ◽  
Dynamics Identification

In this paper the problem of recursive structural dynamics identification from noise-corrupted observations is addressed, and approaches that overcome the weaknesses of current methods, stemming from their underlying deterministic nature and ignorance of the fact that structural systems are inherently continuous-time, are introduced. Towards this end the problem is imbedded into a stochastic framework within which the inadequacy of standard Recursive Least Squares-based approaches is demonstrated. The fact that the continuous-time nature of structural systems necessitates the use of compatible triples of excitation signal type, model structure, and discrete-to-continuous transformation for modal parameter extraction is shown, and two such triples constructed. Based on these, as well as a new stochastic recursive estimation algorithm referred to as Recursive Filtered Least Squares (RFLS) and two other available schemes, a number of structural dynamics identification approaches are formulated and their performance characteristics evaluated. For this purpose structural systems with both well separated and closely spaced modes are used, and emphasis is placed on issues such as the achievable accuracy and resolution, rate of convergence, noise rejection, and computational complexity. The paper is divided into two parts: The problem formulation, the study of the interrelationships among excitation signal type, model structure, and discrete-to-continuous transformation, as well as the formulation of the stochastic identification approaches are presented in the first part, whereas a critical evaluation of their performance characteristics based on both simulated and experimental data is presented in the second.

Damage detection in nonlinear civil structures using kernel principal component analysis

2020 ◽  
Vol 23 (11) ◽  
pp. 2414-2430
Author(s):  
Khaoula Ghoulem ◽  
Tarek Kormi ◽  
Nizar Bel Hadj Ali
Keyword(s):  
Principal Component Analysis ◽  
Damage Detection ◽  
Structural Model ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Correlated Data ◽  
Data Driven ◽  
Kernel Principal Component Analysis ◽  
Structural Systems

In the general framework of data-driven structural health monitoring, principal component analysis has been applied successfully in continuous monitoring of complex civil infrastructures. In the case of linear or polynomial relationship between monitored variables, principal component analysis allows generation of structured residuals from measurement outputs without a priori structural model. The principal component analysis has been widely used for system monitoring based on its ability to handle high-dimensional, noisy, and highly correlated data by projecting the data onto a lower dimensional subspace that contains most of the variance of the original data. However, for nonlinear systems, it could be easily demonstrated that linear principal component analysis is unable to disclose nonlinear relationships between variables. This has naturally motivated various developments of nonlinear principal component analysis to tackle damage diagnosis of complex structural systems, especially those characterized by a nonlinear behavior. In this article, a data-driven technique for damage detection in nonlinear structural systems is presented. The proposed method is based on kernel principal component analysis. Two case studies involving nonlinear cable structures are presented to show the effectiveness of the proposed methodology. The validity of the kernel principal component analysis–based monitoring technique is shown in terms of the ability to damage detection. Robustness to environmental effects and disturbances are also studied.

Sign in / Sign up

Export Citation Format

Share Document