Real time detection of stiffness change using a radial basis function augmented observer formulation

Abstract

Existing methods for structural health monitoring pose a formidable challenge to real time implementation due to the significantly large computational loads. The proposed algorithm is suitable for online applications because it maintains good pattern recognition capabilities while possessing a computationally compact network topology. This study employs the computational efficiency of single layer radial basis function (RBF) approximaters to create a subspace capable of isolating faults in multi-degree of freedom systems which involve coupled and uncoupled stiffness changes in real time. The RBF network transforms the displacement-time history of the varying plant into a decoupled output space which is then compared to a baseline healthy observer which undergoes the same decoupling transformation. The online comparison of the output of the time varying plant and the healthy observer in a decoupled subspace comprises the observer based error function. The error function is shown to not only detect the existence of faults, but also isolate these faults in real time in the presence of base excitation. The method is validated for systems that experience earthquake induced damage, as well as an experimental system using a semi-active independent variable stiffness device which is capable of varying system stiffness in real time. By simply observing the displacement-time history responses, the RBF augmented observer formulation is capable identifying changes in the stiffness at each degree of freedom.

A computationally efficient approach for inverse material characterization combining Gappy POD with direct inversion

“…(40) the rows in Eq. (36) corresponding to nodes where essential boundary conditions are present are set to zero, such that:…”

“…In addition, both examples used the plane stress assumption to reduce the computational expense. A Gaussian radial basis function (RBF) representation was chosen to define the localized elastic modulus variations, which was based upon several other similar works [36][37][38]. The RBF representation of elastic modulus distribution was defined as:…”

An Automatic Detection Method of Nanocomposite Film Element Based on GLCM and Adaboost M1

“…Thus it has been proved that classification effects of class 3 by this model are the best, while those of class 1 and class 2 are lower. In experiment 2, the GABD method has been compared, under the same sample database, to the support vector machine (SVM) [12], BayesNet [13], multilayer perceptron (MLP) neural network [14], RBF neural network [15], and BP neural network [16] models, respectively, in order to further check the detection to the surface texture of polyimide matrix's inorganic nanocomposite thin film. The comparison results of the classification performances of detection models are obtained, as shown in Table 4 and Figure 6.…”

“…(3) Through the comparison experiment we know that the classification performance of GABD is better than that of SVM [12], BayesNet [13], multilayer perceptron (MLP) neural network [14], RBF neural network [15], and BP neural network [16] models under the same sample database. Moreover, it is superior to single classifiers with respect to the accuracy of nanocomposite film detection.…”

A generalized computationally efficient inverse characterization approach combining direct inversion solution initialization with gradient-based optimization

“…It was assumed that a priori knowledge would be available that the elastic modulus distributions to be characterized in the examples would be localized (e.g., as could be expected in applications of damage characterization of civil structures [26] or tumor characterization of biological structures [13]). As such, the snapshot response fields used for the Gappy POD procedure were generated using a Gaussian radial basis function (RBF) representation of the elastic modulus (see [27,28,29] for other similar works utilizing a RBF representation to define localized elastic modulus variations), as:…”