Enhancing Structural Damage Identification Robustness to Noise and Damping With Integrated Bistable and Adaptive Piezoelectric Circuitry

Structural Health Monitoring

2018

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The electromechanical impedance-based damage identification approaches have shown excellent potential in identifying small-sized structural defects, while maintaining simplicity in implementation. The available independent impedance measurement data sets, however, are generally far fewer than the number of required system parameters. As a result, the inverse problem for damage identification is seriously underdetermined, which undermines the reliability of damage prediction since the inverse solution becomes extremely sensitive to even small amount of error in the measurement, especially in practical applications with unavoidable noise and damping influences. This research aims to advance the state of the art by developing a novel approach that (a) enables highly accurate measurement of damage-induced impedance variations against noise and (b) fundamentally improves the underdetermined inverse problem to reliably identify the location and severity of small damages. This new approach utilizes the strongly non-linear bifurcation phenomena in bistable electrical circuits that may exhibit dramatic changes in the response due to small input variations. In this study, an array of bistable circuits is strategically integrated with the structure and piezoelectric transducer so that small damageinduced changes in the piezoelectric impedance can be accurately determined by monitoring whether each circuit exhibits intra-or inter-well responses. This measurement data set is greatly enriched by utilizing an adaptive piezoelectric circuitry with tunable inductor integrated with the monitored structure, which introduces more degrees of freedom into the system. By selectively tuning the inductance values, the dynamic characteristic of the electromechanically coupled system can be altered, thereby one can significantly increase the number of impedance variation measurements for the same damage profile. The enriched data set is utilized to fundamentally improve the underdetermined inverse problem for damage identification. A series of numerical and experimental damage identification studies verify that the proposed methodology can significantly enhance the accuracy and reliability of impedance-based damage identification.

Section: Introductionmentioning

confidence: 92%

Section: Impedance Change Measurement Using Bifurcations In Bistable mentioning

confidence: 99%

Electromechanical impedance-based damage identification enhancement using bistable and adaptive piezoelectric circuitry

Structural Health Monitoring

2018

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“…The bistable circuit involves saddle-node bifurcation that activates sudden transitions between the intrawell and interwell oscillations. As a result, bistable circuits have been applied for various bifurcation-based applications [37,39,[55][56][57][58][59]. Since this bistable circuit design exhibits negligible backward coupling due to the characteristics of op-amp, it is suitable to utilize as a signal conditioning device that has little effect on the input signal [37].…”

Section: Experimental Investigationsmentioning

confidence: 99%

Online Signal Denoising Using Adaptive Stochastic Resonance in Parallel Array and Its Application to Acoustic Emission Signals

Harne

Journal of Vibration and Acoustics

2021

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Signal denoising has been significantly explored in various engineering disciplines. In particular, structural health monitoring applications generally aim to detect weak anomaly responses (including acoustic emission) generated by incipient damage, which are easily buried in noise. Among various approaches, stochastic resonance (SR) has been widely adopted for weak signal detection. While many advancements have been focused on identifying useful information from the frequency domain by optimizing parameters in a post-processing environment to activate SR, it often requires detailed information about the original signal a priori, which is hardly assessed from signals overwhelmed by noise. This research presents a novel online signal denoising strategy by utilizing SR in a parallel array of bistable systems. The original noisy input with additionally applied noise is adaptively scaled, so that the total noise level matches the optimal level that is analytically predicted from a generalized model to robustly enhance signal denoising performance for a wide range of input amplitudes that are often not known in advance. Thus, without sophisticated post-processing procedures, the scaling factor is straightforwardly determined by the analytically estimated optimal noise level and the ambient noise level, which is one of the few quantities that can be reliably assessed from noisy signals in practice. Along with numerical investigations that demonstrate the operational principle and the effectiveness of the proposed strategy, experimental validation of denoising acoustic emission signals by employing a bistable Duffing circuit system exemplifies the promising potential of implementing the new approach for enhancing online signal denoising in practice.

“…By leveraging unmistakable and sudden variations in sensor behavior due to subtle perturbations to the system which induce the bifurcation, the bifurcation-based sensing methods yield significantly enhanced resolution and sensitivity compared to conventional, linear dynamics-based detection methods. To introduce these novel and high performance detection principles into the context of meso-/macro-scale mechanical and civil structural health and condition monitoring, the authors recently integrated double-well Duffing circuits with the monitored systems [45,46]. In this way, small changes in the (assumed) linear dynamics of the monitored structures may activate more dramatic shifts in the circuit behaviors, providing a robust and sensitive framework for structural monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…1. Moreover, it is the double-well Duffing analog circuit that has been recently utilized as a bifurcation-based sensing device for detecting structural changes and damage in macroscale structures [45,46]. Thus, the analysis on stochastic and dynamic activation of the bifurcation directly provides an important means of characterizing the dynamics of practical bifurcation-based sensor platform.…”

Section: Introductionmentioning

confidence: 99%

Predicting Non-Stationary and Stochastic Activation of Saddle-Node Bifurcation

Harne

Journal of Computational and Nonlinear Dynamics

2016

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Accurately predicting the onset of large behavioral deviations associated with saddle-node bifurcations is imperative in a broad range of sciences and for a wide variety of purposes, including ecological assessment, signal amplification, and microscale mass sensing. In many such practices, noise and non-stationarity are unavoidable and ever-present influences. As a result, it is critical to simultaneously account for these two factors toward the estimation of parameters that may induce sudden bifurcations. Here, a new analytical formulation is presented to accurately determine the probable time at which a system undergoes an escape event as governing parameters are swept toward a saddle-node bifurcation point in the presence of noise. The double-well Duffing oscillator serves as the archetype system of interest since it possesses a dynamic saddle-node bifurcation. The stochastic normal form of the saddle-node bifurcation is derived from the governing equation of this oscillator to formulate the probability distribution of escape events. Non-stationarity is accounted for using a time-dependent bifurcation parameter in the stochastic normal form. Then, the mean escape time is approximated from the probability density function (PDF) to yield a straightforward means to estimate the point of bifurcation. Experiments conducted using a double-well Duffing analog circuit verifies that the analytical approximations provide faithful estimation of the critical parameters that lead to the non-stationary and noise-activated saddle-node bifurcation.