scholarly journals Adaptive One-Class Support Vector Machine for Damage Detection in Structural Health Monitoring

2017 ◽  
pp. 42-57 ◽  
Author(s):  
Ali Anaissi ◽  
Nguyen Lu Dang Khoa ◽  
Samir Mustapha ◽  
Mehrisadat Makki Alamdari ◽  
Ali Braytee ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Support Vector ◽  
Structural Health

Fatigue damage detection from imbalanced inspection data of Lamb wave

2021 ◽  
pp. 147592172110152
Author(s):  
Jingjing He ◽  
Ziwei Fang ◽  
Jie Liu ◽  
Fei Gao ◽  
Jing Lin
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Lamb Wave ◽  
Sampling Technique ◽  
Support Vector ◽  
Small Scale ◽  
Detection Range ◽  
Structural Health ◽  
Inspection Data

The core of structural health monitoring is to provide a real-time monitoring, inspection, and damage detection of structures. As one of the most promising technology to structural health monitoring, the Lamb wave method has attracted interest because it is sensitive to small-scale damage with a long detection range. However, in many real-world structural health monitoring applications, the nature of the problem implies structures work under normal condition in most of its operating phases; therefore, classes of data collected are not equally represented. The predictive capability of damage detection algorithms may significantly be impaired by class imbalance. This article presents a damage detection method using imbalanced inspection data which is collected through Lamb wave detection. Aiming at maximizing detection accuracy, an improved synthetic minority over-sampling technique using three-point triangle (triangle synthetic minority over-sampling technique) is proposed to conduct the over-sampling procedure and increase the number of minority samples. The iterative-partitioning filter is employed to remove the noisy examples which may be introduced by triangle synthetic minority over-sampling technique. Three conventional classification methods, namely, support vector machine, decision tree, and k-nearest neighbor, are used to perform the damage detection. A fatigue crack detection test using Lamb wave is performed to demonstrate the overall procedure of the proposed method. Three damage sensitive features, namely, normalized amplitude, correlation coefficient, and normalized energy, are extracted from signals as datasets. A cross-validation is performed to verify the performance of the proposed method for crack size identification.

Structural Health Monitoring With Autoregressive Support Vector Machines

2009 ◽  
Vol 131 (2) ◽  
Author(s):  
Luke Bornn ◽  
Charles R. Farrar ◽  
Gyuhae Park ◽  
Kevin Farinholt
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Linear Models ◽  
Vibration Sensor ◽  
Structural Vibration ◽  
Support Vector ◽  
Detection Techniques ◽  
Structural Health

The use of statistical methods for anomaly detection has become of interest to researchers in many subject areas. Structural health monitoring in particular has benefited from the versatility of statistical damage-detection techniques. We propose modeling structural vibration sensor output data using nonlinear time-series models. We demonstrate the improved performance of these models over currently used linear models. Whereas existing methods typically use a single sensor’s output for damage detection, we create a combined sensor analysis to maximize the efficiency of damage detection. From this combined analysis we may also identify the individual sensors that are most influenced by structural damage.

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