Application of a novel time series based method for damage detection, localization and quantification using output only acceleration data

Author(s):  
Q Mei ◽  
M Gul
Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1983 ◽  
Author(s):  
Hadi Kordestani ◽  
Chunwei Zhang

The Savitzky–Golay filter (SGF) is a time-domain technique that determines a trend line for a signal. The direct application of SGF for damage localization and quantification is investigated in this paper. Therefore, a single-stage trend line-based damage detection method employing SGF is proposed in which the damage is located and quantified at the bridge under moving load. A simply supported beam under moving sprung mass is numerically simulated to verify the proposed method. Four different velocities and five different single- and multi-damage scenarios are considered. The acceleration data along the beam are obtained, manually polluted with noise and their trend lines are then determined using SGF. The results show that the proposed method can accurately locate and quantify the damage using these trend lines. It is proved that the proposed method is insensitive to the noise and velocity variation in which having a constant velocity is a hard task before and after damage. Additionally, defining a normalization factor and fitting a Gaussian curve to this factor provide an estimation for the baseline and therefore, it categorizes the proposed method as baseline-free method.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Qun Yang ◽  
Dejian Shen ◽  
Wencai Du ◽  
Weijun Li

Author(s):  
Chin-Hsiung Loh ◽  
Min-Hsuan Tseng ◽  
Shu-Hsien Chao

One of the important issues to conduct the damage detection of a structure using vibration-based damage detection (VBDD) is not only to detect the damage but also to locate and quantify the damage. In this paper a systematic way of damage assessment, including identification of damage location and damage quantification, is proposed by using output-only measurement. Four level of damage identification algorithms are proposed. First, to identify the damage occurrence, null-space and subspace damage index are used. The eigenvalue difference ratio is also discussed for detecting the damage. Second, to locate the damage, the change of mode shape slope ratio and the prediction error from response using singular spectrum analysis are used. Finally, to quantify the damage the RSSI-COV algorithm is used to identify the change of dynamic characteristics together with the model updating technique, the loss of stiffness can be identified. Experimental data collected from the bridge foundation scouring in hydraulic lab was used to demonstrate the applicability of the proposed methods. The computation efficiency of each method is also discussed so as to accommodate the online damage detection.


2003 ◽  
Vol 2 (2) ◽  
pp. 161-168 ◽  
Author(s):  
Michèle Basseville ◽  
Laurent Mevel ◽  
Antonio Vecchio ◽  
Bart Peeters ◽  
Herman Van der Auweraer

Author(s):  
M. Farid Golnaraghi ◽  
DerChyan Lin ◽  
Paul Fromme

Abstract This paper is a preliminary study applying nonlinear time series analysis to crack detection in gearboxes. Our investigations show that the vibration signal emerging from a gearbox is chaotic. Appearance of a crack in a gear tooth alters this response and hence the chaotic signature. We used correlation dimension and Lyapunov exponents to quantify this change. The main goal of this study is to point out the great potential of these methods in detection of cracks and faults in machinery.


2014 ◽  
Vol 578-579 ◽  
pp. 1020-1023
Author(s):  
Jing Zhou Lu ◽  
Jia Chen Wang ◽  
Xu Zhu

In this paper, we introduce a set of techniques for time series analysis based on principal component analysis (PCA). Firstly, the autoregressive (AR) model is established using acceleration response data, and the root mean squared error (RMSE) of AR model is calculated based on PCA. Then a new damage sensitive feature (DSF) based on the AR coefficients is presented. To test the efficacy of the damage detection and localization methodologies, the algorithm has been tested on the analytical and experimental results of a three-story frame structure model of the Los Alamos National Laboratory. The result of the damage detection indicates that the algorithm is able to identify and localize minor to severe damage as defined for the structure. It shows that the suggested method can lead to less amount of computing time, high suitability and identification accuracy.


2018 ◽  
Vol 167 ◽  
pp. 549-566 ◽  
Author(s):  
Giacomo Bernagozzi ◽  
Suparno Mukhopadhyay ◽  
Raimondo Betti ◽  
Luca Landi ◽  
Pier Paolo Diotallevi

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