Application of Frequency Domain ARX Models and Extreme Value Statistics to Impedance-Based Damage Detection

Aerospace ◽  
2003 ◽  
Author(s):  
Timothy R. Fasel ◽  
Hoon Sohn ◽  
Gyuhae Park ◽  
Charles R. Farrar
Keyword(s):  
Damage Detection ◽  
Frequency Domain ◽  
Extreme Value ◽  
Superior Performance ◽  
Extreme Value Statistics ◽  
Arx Model ◽  
Building Model ◽  
System Input ◽  
Auto Regressive ◽  
Non Gaussian

In this paper, the applicability of an auto-regressive model with exogenous inputs (ARX) in the frequency domain to structural health monitoring (SHM) is established. Damage sensitive features that explicitly consider nonlinear system input/output relationships are extracted from the ARX model. Furthermore, because of the non-Gaussian nature of the extracted features, Extreme Value Statistics (EVS) is employed to develop a robust damage classifier. EVS provides superior performance to standard statistical methods because the data of interest are in the tails (extremes) of the damage sensitive feature distribution. The suitability of the ARX model, combined with EVS, to nonlinear damage detection is demonstrated with an impedance-based method that uses piezoelectric (PZT) material as both actuators and sensors. The analyzed data is obtained from a laboratory experiment of a three-story building model.

Damage detection in mechanical structures using extreme value statistics

2002 ◽  
Author(s):  
Keith Worden ◽  
David W. Allen ◽  
Hoon Sohn ◽  
Charles R. Farrar
Keyword(s):  
Damage Detection ◽  
Extreme Value ◽  
Extreme Value Statistics

Active sensing using impedance-based ARX models and extreme value statistics for damage detection

2005 ◽  
Vol 34 (7) ◽  
pp. 763-785 ◽  
Author(s):  
Timothy R. Fasel ◽  
Hoon Sohn ◽  
Gyuhae Park ◽  
Charles R. Farrar
Keyword(s):  
Damage Detection ◽  
Extreme Value ◽  
Extreme Value Statistics ◽  
Active Sensing

scholarly journals Automatic Modulation Classification Based on Deep Feature Fusion for High Noise Level and Large Dynamic Input

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2117
Author(s):  
Hui Han ◽  
Zhiyuan Ren ◽  
Lin Li ◽  
Zhigang Zhu
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Noise Level ◽  
Background Noise ◽  
Dynamic Range ◽  
Feature Fusion ◽  
Superior Performance ◽  
Modulation Classification ◽  
High Background ◽  
Automatic Modulation Classification

Automatic modulation classification (AMC) is playing an increasingly important role in spectrum monitoring and cognitive radio. As communication and electronic technologies develop, the electromagnetic environment becomes increasingly complex. The high background noise level and large dynamic input have become the key problems for AMC. This paper proposes a feature fusion scheme based on deep learning, which attempts to fuse features from different domains of the input signal to obtain a more stable and efficient representation of the signal modulation types. We consider the complementarity among features that can be used to suppress the influence of the background noise interference and large dynamic range of the received (intercepted) signals. Specifically, the time-series signals are transformed into the frequency domain by Fast Fourier transform (FFT) and Welch power spectrum analysis, followed by the convolutional neural network (CNN) and stacked auto-encoder (SAE), respectively, for detailed and stable frequency-domain feature representations. Considering the complementary information in the time domain, the instantaneous amplitude (phase) statistics and higher-order cumulants (HOC) are extracted as the statistical features for fusion. Based on the fused features, a probabilistic neural network (PNN) is designed for automatic modulation classification. The simulation results demonstrate the superior performance of the proposed method. It is worth noting that the classification accuracy can reach 99.8% in the case when signal-to-noise ratio (SNR) is 0 dB.

scholarly journals A frequency‐domain approach for damage detection in welded structures

2021 ◽  
Author(s):  
Camilla Ronchei ◽  
Sabrina Vantadori ◽  
Andrea Carpinteri ◽  
Ignacio Iturrioz ◽  
Roberto Issopo Rodrigues ◽  
...  
Keyword(s):  
Damage Detection ◽  
Frequency Domain ◽  
Welded Structures ◽  
Frequency Domain Approach

On the spectral shape of SEP events: An extreme value statistics approach

2013 ◽  
Author(s):  
M. Laurenza ◽  
G. Consolini ◽  
M. Storini ◽  
A. Damiani
Keyword(s):  
Extreme Value ◽  
Spectral Shape ◽  
Extreme Value Statistics

Hermite Extreme Value Estimation of Non-Gaussian Wind Load Process on a Long-Span Roof Structure

2014 ◽  
Vol 140 (9) ◽  
pp. 04014061 ◽  
Author(s):  
M. F. Huang ◽  
Wenjuan Lou ◽  
Xiaotao Pan ◽  
C. M. Chan ◽  
Q. S. Li
Keyword(s):  
Wind Load ◽  
Extreme Value ◽  
Long Span ◽  
Roof Structure ◽  
Value Estimation ◽  
Non Gaussian
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