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.

Nonlinear Modeling of Cracked Beams for Impedance Based Structural Health Monitoring

2017 ◽  
Author(s):  
Naserodin Sepehry ◽  
Firooz Bakhtiari-Nejad ◽  
Mahnaz Shamshirsaz ◽  
Weidong Zhu
Keyword(s):  
Structural Health Monitoring ◽  
Linear System ◽  
Frequency Response ◽  
Frequency Domain ◽  
Health Monitoring ◽  
Impedance Method ◽  
Breathing Crack ◽  
Electromechanical Impedance ◽  
Structural Health ◽  
System Equations

One of the main objectives of the structural health monitoring by piezoelectric wafer active sensor (PWAS) using electromechanical impedance method is continuously damage detection applications. In present work impedance method of beam structure is considered and the effect of early crack using breathing crack modeling is studied. In order to model the effect of a crack in beam, the beam is connected with a rotational spring in crack location. The Rayleigh–Ritz method is used to generate ordinary differential equation of cracked beam. Firstly, only open crack is considered that this is leads to linear system equation. In linear system, time domain system equations are converted to frequency domain, and then impedance of PWAS in frequency domain is calculated. Secondly, the breathing crack is modeled to be fully open or fully closed. This phenomenon leads to the nonlinear system equations. These nonlinear equations are solved using pseudo-arc length continuation scheme and collocation method for any harmonic voltage applied to actuator. Then impedance of PWAS is calculated. Two methods are used to detect early crack using breathing crack modeling on PWAS impedance. At the first, frequency response of breathing crack in the frequency range with its sub-harmonics is calculated. Second, only frequency response of one harmonic is computed with its super-harmonics. Finally, the detection method of linear is compared with nonlinear model.

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.

Uncertainty Modeling and Quantification for Structural Health Monitoring Features Derived from Frequency Response Estimation

2013 ◽  
Vol 569-570 ◽  
pp. 1148-1155
Author(s):  
Zhu Mao ◽  
Michael D. Todd
Keyword(s):  
Structural Health Monitoring ◽  
Frequency Response ◽  
Frequency Domain ◽  
Health Monitoring ◽  
Structural Engineering ◽  
Hypothesis Test ◽  
Transfer Model ◽  
Type I ◽  
Operating Characteristics ◽  
Structural Health

System identification in the frequency domain plays a fundamental role in many aspects of mechanical and structural engineering. Frequency domain approaches typically involve estimation of a transfer function, whether it is the usual frequency response function (FRF) or an output-to-output transfer model (transmissibility). The field of structural health monitoring, which involves extracting and classifying features mined from in-sit structural performance data for the purposes of damage condition assessment, has exploited many features for this purpose that inherently are derived from estimations of frequency domain models such as the FRF or transmissibility. Structural health monitoring inevitably involves a hypothesis test at the classification stage such as the (common) binary question: are the features mined from data derived from a reference condition or from data derived from a different (test) condition? Inevitably, this decision involves stochastic data, as any such candidate feature is compromised by error, which we categorize as (i) operational and environmental, (ii) measurement, and (iii) computational/estimation. Regardless of source, this noise leads to the propagation of error, resulting in possible false positive (Type I) errors in the classification. As such, the quantification of uncertainty in the estimation of such features is tantamount to making informed decisions based on a hypothesis test. This paper will demonstrate several statistical models that describe the uncertainty in FRF estimation and will compare their performance to features derived from them for the purposes of detecting damage, with ultimate performance evaluated by receiver operating characteristics (ROCs). A simulation and a plate subject to single-input/single-output vibration testing will serve as the comparison testbeds.

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