Identification of Structural Dynamics Models Using Wavelet-Generated Impulse Response Data

1998 ◽  
Vol 120 (1) ◽  
pp. 261-266 ◽  
Author(s):  
A. N. Robertson ◽  
K. C. Park ◽  
K. F. Alvin
Keyword(s):  
System Identification ◽  
Impulse Response ◽  
Structural Dynamics ◽  
Wavelet Transforms ◽  
Mode Shapes ◽  
Discrete Wavelet ◽  
Impulse Response Functions ◽  
Narrow Frequency Band ◽  
Structural System Identification ◽  
Dynamics Models

This paper addresses the use of discrete wavelet transforms for the identification of structural dynamics models. First, the discrete temporal impulse response functions are obtained from vibration records by the discrete wavelet transform (DWT). They are then utilized for system realizations. From the realized state space models, structural modes, mode shapes and damping parameters are extracted. Attention has been focused on a careful comparison of the present DWT system identification approach to the FFT-based approach. Numerical examples demonstrate that the present DWT-based structural system identification procedure is a serious alternative to the FFT-based procedure, and outperforms FFT methods for narrow frequency-band inputs.

Identification of Structural Dynamics Models Using Wavelet-Generated Impulse Response Data

1995 ◽  
Author(s):  
A. N. Robertson ◽  
K. C. Park ◽  
K. F. Alvin
Keyword(s):  
System Identification ◽  
Impulse Response ◽  
Structural Dynamics ◽  
Wavelet Transforms ◽  
Singular Values ◽  
Mode Shapes ◽  
Discrete Wavelet ◽  
Impulse Response Functions ◽  
Structural System Identification ◽  
Dynamics Models

Abstract This paper addresses the use of discrete wavelet tranforms for the identification of structural dynamics models. First, the discrete temporal impulse response functions are obtained from vibration records by the discrete wavelet transforms (DWTs), which are then utilized for system realizations. From the realized state space models, structural modes, mode shapes and damping parameters are extracted. Attention has been focused on a careful comparison of the present DWT system identification approach to the FFT-based approach and a rational criterion for truncating realized singular values. Numerical examples demonstrate that the present DWT-based structural system identification procedure outperforms the FFT-based procedure.

A Theoretical Analysis of Transient Sound Radiation From a Clamped Circular Plate

1984 ◽  
Vol 51 (1) ◽  
pp. 41-47 ◽  
Author(s):  
A. Akay ◽  
M. Tokunaga ◽  
M. Latcha
Keyword(s):  
Theoretical Analysis ◽  
Circular Plate ◽  
Impulse Response ◽  
Sound Radiation ◽  
Free Vibrations ◽  
Mode Shapes ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Surface Integral ◽  
Pressure Impulse

A theoretical analysis of transient sound radiation from a clamped circular plate is given using a pressure impulse response method. The vibration response of the plate to a transient point force is obtained. The modal pressure impulse response functions for the plate are derived from the Rayleigh surface integral and numerically convoluted with the modal acceleration response of the plate. The impulse response functions are closely related to the mode shapes and the geometry of the problem. They relate the spatial domain to the temporal domain of the pressure waves. The pressure impulse response waveforms are given for a number of plate modes and the changes in the waveforms with distance from the plate are shown. Sound radiation due to forced and free vibrations of the plate are discussed.

Extraction of Impulse Response Data via Wavelet Transform for Structural System Identification

1998 ◽  
Vol 120 (1) ◽  
pp. 252-260 ◽  
Author(s):  
A. N. Robertson ◽  
K. C. Park ◽  
K. F. Alvin
Keyword(s):  
Wavelet Transform ◽  
Impulse Response ◽  
Gibbs Phenomenon ◽  
Impulse Response Functions ◽  
Structural Dynamic ◽  
Wavelet Method ◽  
Response Characteristics ◽  
Input Signals ◽  
Response Modes ◽  
Structural System Identification

This paper presents a wavelet transform-based method of extracting the impulse response characteristics from the measured disturbances and response histories of linear structural dynamic systems. The proposed method is found to be effective in determining the impulse response functions for systems subjected to harmonic (narrow frequency-band) input signals and signals with sharp discontinuities, thus alleviating the Gibbs phenomenon encountered in FFT methods. When the system is subjected to random burst input signals for which the FFT methods are known to perform well, the proposed wavelet method performs equally well with a fewer number of ensembles than FFT-based methods. For completely random input signals, both the wavelet and FFT methods experience difficulties, although the wavelet method appears to perform somewhat better in tracing the fundamental response modes.

scholarly journals Performance of Damage Identification Based on Directional Wavelet Transforms and Entopic Weights Using Experimental Shearographic Testing Results

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 714
Author(s):  
Andrzej Katunin
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Wavelet Transforms ◽  
Damage Identification ◽  
Experimental Tests ◽  
Mode Shapes ◽  
Discrete Wavelet ◽  
Industrial Sectors ◽  
Directional Wavelet ◽  
Susceptibility To Noise

The paper aims to analyze the performance of the damage identification algorithms using the directional wavelet transforms, which reveal higher sensitivity for various orientations of spatial damage together with lower susceptibility to noise. In this study, the algorithms based on the dual-tree, the double-density, and the dual-tree double-density wavelet transforms were considered and compared to the algorithm based on the discrete wavelet transform. The performed analyses are based on shearographic experimental tests of a composite plate with artificially introduced damage at various orientations. It was shown that the directional wavelet transforms are characterized by better performance in damage identification problems than the basic discrete wavelet transform. Moreover, the proposed approach based on entropic weights applicable to the resulting sets of the detail coefficients after decomposition of mode shapes can be effectively used for automatic selection and emphasizing those sets of the detail coefficients, which contain relevant diagnostic information about damage. The proposed processing method allows raw experimental results from shearography to be significantly enhanced. The developed algorithms can be successfully implemented in a shearographic testing for enhancement of a sensitivity to damage during routine inspections in various industrial sectors.

Fault detection in rotor system by discrete wavelet neural network algorithm

2021 ◽  
pp. 107754632110307
Author(s):  
K Babu Rao ◽  
D Mallikarjuna Reddy
Keyword(s):  
Neural Network ◽  
Wavelet Transforms ◽  
Groove Depth ◽  
Mode Shapes ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Response Curves ◽  
Crack Location ◽  
Stepped Shaft ◽  
Rotational Response

This study identifies a method for detection of irregularities such as open cracks or grooves on a rotating stepped shaft with multiple discs, based on the wavelet transforms. Cracks are represented as reduction in diameter of shaft (groove) with small width. Single as well as multiple grooves are considered on stepped shaft at locations of stress concentration. Translational or rotational response curves/mode shapes are extracted from finite element analysis of rotors with and without grooves. Discrete and continuous 1D wavelet transforms are applied on resultant response curve or mode shapes. The results show that rotational response curves or mode shapes are more sensitive to shaft cracks and key contributors to identify the location of cracks than translation response curves or mode shapes. Discrete wavelet transforms are accurate enough to locate the groove of smaller size. Effectiveness of detection by wavelets transforms is analysed for single as well as multiple grooves with increase in groove depth. Increase in groove depth can be quantified by increase in wavelet coefficient, and it can be an indicator. White Gaussian noise with low signal-to-noise ratio is added to response curves and analysed for crack location identification. Intelligent techniques such as artificial neural networks are used to quantify the location and depth of crack. Discrete wavelet transforms coefficients are provided as input to the neural network. Feed forward artificial neural networks are trained with Levenberg–Marquardt back propagation algorithm. Trained networks are able to quantify the crack location and depth accurately.

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