Structural Health Monitoring by Time Series Analysis and Statistical Distance Measures

2021 ◽  
Author(s):  
Alireza Entezami
Keyword(s):  
Time Series ◽  
Structural Health Monitoring ◽  
Time Series Analysis ◽  
Health Monitoring ◽  
Distance Measures ◽  
Structural Health ◽  
Series Analysis ◽  
Statistical Distance

Structural Health Monitoring in Smart Structures Through Time Series Analysis

2008 ◽  
Vol 7 (3) ◽  
pp. 231-244 ◽  
Author(s):  
Samuel da Silva ◽  
Milton Dias Júnior ◽  
Vicente Lopes Junior
Keyword(s):  
Time Series ◽  
Structural Health Monitoring ◽  
Time Series Analysis ◽  
Health Monitoring ◽  
Smart Structures ◽  
Structural Health ◽  
Series Analysis

Structural Health Monitoring Using Statistical Pattern Recognition Techniques

2001 ◽  
Vol 123 (4) ◽  
pp. 706-711 ◽  
Author(s):  
Hoon Sohn ◽  
Charles R. Farrar ◽  
Norman F. Hunter ◽  
Keith Worden
Keyword(s):  
Time Series ◽  
Pattern Recognition ◽  
Structural Health Monitoring ◽  
Time Series Analysis ◽  
Health Monitoring ◽  
Statistical Pattern Recognition ◽  
Pattern Recognition Techniques ◽  
Series Analysis ◽  
Statistical Pattern ◽  
Auto Regressive

This paper casts structural health monitoring in the context of a statistical pattern recognition paradigm. Two pattern recognition techniques based on time series analysis are applied to fiber optic strain gauge data obtained from two different structural conditions of a surface-effect fast patrol boat. The first technique is based on a two-stage time series analysis combining Auto-Regressive (AR) and Auto-Regressive with eXogenous inputs (ARX) prediction models. The second technique employs an outlier analysis with the Mahalanobis distance measure. The main objective is to extract features and construct a statistical model that distinguishes the signals recorded under the different structural conditions of the boat. These two techniques were successfully applied to the patrol boat data clearly distinguishing data sets obtained from different structural conditions.

Use of Time-Series Predictive Models for Piezoelectric Active-Sensing in Structural Health Monitoring Applications

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Eloi Figueiredo ◽  
Gyuhae Park ◽  
Kevin M. Farinholt ◽  
Charles R. Farrar ◽  
Jung-Ryul Lee
Keyword(s):  
Time Series ◽  
Structural Health Monitoring ◽  
Frequency Response ◽  
Frequency Domain ◽  
Health Monitoring ◽  
Active Sensing ◽  
Response Functions ◽  
Active Sensors ◽  
Frequency Response Functions ◽  
Structural Health

In this paper, time domain data from piezoelectric active-sensing techniques is utilized for structural health monitoring (SHM) applications. Piezoelectric transducers have been increasingly used in SHM because of their proven advantages. Especially, their ability to provide known repeatable inputs for active-sensing approaches to SHM makes the development of SHM signal processing algorithms more efficient and less susceptible to operational and environmental variability. However, to date, most of these techniques have been based on frequency domain analysis, such as impedance-based or high-frequency response functions-based SHM techniques. Even with Lamb wave propagations, most researchers adopt frequency domain or other analysis for damage-sensitive feature extraction. Therefore, this study investigates the use of a time-series predictive model which utilizes the data obtained from piezoelectric active-sensors. In particular, time series autoregressive models with exogenous inputs are implemented in order to extract damage-sensitive features from the measurements made by piezoelectric active-sensors. The test structure considered in this study is a composite plate, where several damage conditions were artificially imposed. The performance of this approach is compared to that of analysis based on frequency response functions and its capability for SHM is demonstrated.

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