The Effect of Detection Feature Type on Excitations Bred for Active Sensing in Structural Health Monitoring

2009 ◽  
Vol 20 (11) ◽  
pp. 1307-1327 ◽  
Author(s):  
C.C. Olson ◽  
L.A. Overbey ◽  
M.D. Todd
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Active Sensing ◽  
Structural Health

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.

scholarly journals Statistical guided-waves-based structural health monitoring via stochastic non-parametric time series models

2021 ◽  
pp. 147592172110245
Author(s):  
Ahmad Amer ◽  
Fotis P Kopsaftopoulos
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Guided Waves ◽  
State Of The Art ◽  
Time Series Models ◽  
Active Sensing ◽  
Structural Health

Damage detection in active-sensing, guided-waves-based structural health monitoring (SHM) has evolved through multiple eras of development during the past decades. Nevertheless, there still exist a number of challenges facing the current state-of-the-art approaches, both in the industry as well as in research and development, including low damage sensitivity, lack of robustness to uncertainties, need for user-defined thresholds, and non-uniform response across a sensor network. In this work, a novel statistical framework is proposed for active-sensing SHM based on the use of ultrasonic guided waves. This framework is based on stochastic non-parametric time series models and their corresponding statistical properties in order to readily provide healthy confidence bounds and enable accurate and robust damage detection via the use of appropriate statistical decision-making tests. Three such methods and corresponding statistical quantities (test statistics) along with decision-making schemes are formulated and experimentally assessed via the use of three coupons with different levels of complexity: an Al plate with a growing notch, a carbon fiber-reinforced plastic (CFRP) plate with added weights to simulate local damage, and the CFRP panel used in the Open Guided Waves project, all fitted with piezoelectric transducers under a pitch-catch configuration. The performance of the proposed methods is compared to that of state-of-the-art time-domain damage indices (DIs). The results demonstrate the increased detection sensitivity and robustness of the proposed methods, with better tracking capability of damage evolution compared to conventional approaches, even for damage-non-intersecting actuator–sensor paths. In particular, the Z statistic emerges as the best damage detection metric compared to conventional DIs, as well as the other proposed statistics. Overall, the proposed statistics in this study promise greater damage sensitivity across different components, with enhanced robustness to uncertainties, as well as user-friendly application.

scholarly journals Design of a wireless active sensing unit for structural health monitoring

2004 ◽  
Author(s):  
Jerome P. Lynch ◽  
Arvind Sundararajan ◽  
Kincho H. Law ◽  
Hoon Sohn ◽  
Charles R. Farrar
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Active Sensing ◽  
Structural Health

Reliability of crack quantification via acousto-ultrasound active-sensing structural health monitoring using surface-mounted PZT actuators/sensors

2020 ◽  
pp. 147592172092153
Author(s):  
Susheel Kumar Yadav ◽  
Spandan Mishra ◽  
Fotis Kopsaftopoulos ◽  
Fu-Kuo Chang
Keyword(s):  
Wave Propagation ◽  
Structural Health Monitoring ◽  
Sensor Network ◽  
Health Monitoring ◽  
Active Sensing ◽  
Structural Components ◽  
Network Configuration ◽  
Structural Health ◽  
Damage Quantification ◽  
Propagation Paths

This work presents the introduction and experimental investigation of an active-sensing acousto-ultrasound structural health monitoring approach for damage size quantification based on piezoelectric sensors/actuators mounted on multiple seemingly identical structural components. The objective of this work is to determine how reliable the damage diagnostics can be from one component to another similar (nominally identical) component using surface-mounted PZT (lead zirconate titanate) sensors/actuators, and also to evaluate how sensitive a sensor network configuration in terms of the number of sensors/actuators is with respect to its detection reliability. Extensive crack growth experiments on multiple identical coupons outfitted with the same sensor network configuration under cyclic loads were conducted to assess the damage quantification reliability from one coupon to another using the same diagnostic algorithm. The results of the study indicate that the crack size estimates obtained from the active-sensing structural health monitoring system can vary within the population of identical structural components (coupons), but the difference in quantifying damage among coupons decreases with the increase in the number of sensors and actuators used, that is, wave propagation paths. Furthermore, it is shown that the diagnostic results in terms of damage quantification converge with the increase in the number of sensors. The results of the study indicate that the diagnostic approach using a multi-path sensor network can reduce the damage quantification error from one component to another within a “hotspot” configuration (damage location is known or suspected a priori). Finally, the results of this study indicate that the more wave propagation paths used in the diagnostic active-sensing algorithm, the more reliable the damage quantification results are, provided that the same sensor network is used and installed at nominally identical locations for all coupons.

SMART Platform for Structural Health Monitoring of Sensorised Stiffened Composite Panels

2012 ◽  
Vol 525-526 ◽  
pp. 581-584 ◽  
Author(s):  
Z. Sharif-Khodaei ◽  
M. Ghajari ◽  
M.H. Aliabadi ◽  
A. Apicella
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Probability Of Detection ◽  
Composite Panel ◽  
Active Sensing ◽  
Main Function ◽  
Structural Health ◽  
Force Magnitude ◽  
Passive Sensing ◽  
Diagnostic Capabilities

A SMART Platform is developed based on sensor readings for Structural Health Monitoring of a stiffened composite panel. The platforms main function is divided into three categories: Passive sensing, Active sensing and Optimal sensor positioning. The platform has self-diagnostic capabilities, i.e. prior to its application the health of the sensors and their connection will be checked to avoid any false alarm. Passive sensing results in impact location and force magnitude detection. Active sensing is performed for damage detection. It results in detecting the damage location and severity. Finally the optimal sensor location can be provided given the number of sensors and probability of detection value. This platform is the first step in applying the developed SHM methodologies to real size structures in service load conditions.

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