The piezoelectric impedance-based method for damage detection is a promising approach in structural health monitoring by virtue of its potential to detect small-sized damages with a low-cost measurement circuit that enables remote monitoring. The amount of available impedance data, however, is generally far less than the number of required system parameters, which results in a highly underdetermined inverse problem for identifying the location and severity of the damage. This numerical ill-conditioning undermines the accuracy and reliability of damage prediction, particularly in practical implementations in which measurement noise and baseline modeling error are unavoidable. In this research paper, we propose a new concept to enrich the impedance measurement by incorporating an adaptive piezoelectric circuitry in the structure. This circuitry alters the dynamics of the integrated system, and by systematically tuning the inductance value, one can significantly increase the number of measurement sets. Thus, the previous seriously underdetermined inverse problem can be notably improved. As a result, the new method yields significantly more accurate damage location and severity identification. A numerical example of the damage prediction of a fixed-fixed beam using the spectral element method demonstrates the effectiveness of the proposed approach. The concept is also verified via experimental investigations.

Many common environmental vibration sources exhibit low and broad frequency spectra. In order to exploit such excitations, energy harvesting architectures utilizing nonlinearity, especially bistability, have been extensively explored as a promising energy source for self-powered small-scale electric devices. For such devices, the energetic interwell oscillations between their stable equilibria can provide enhanced power harvesting capability over a wider bandwidth compared to the linear counterpart. Yet, one of the limitations of these nonlinear architectures is that the interwell oscillation regime may not be readily activated for low excitation level that is not sufficient to overcome the potential energy barrier, thus resulting in low amplitude intrawell response, which provides poor energy harvesting performance. This research investigates a multi-degree of freedom (MDOF) vibration energy harvesting system that leverages magnetically coupled bistable and linear harvesters. It presents novel in-depth insights into capitalizing on a passive mechanism that not only facilitates the energetic interwell response for relatively low excitation amplitudes and frequencies than that may be required for conventional bistable harvester by passively and adaptively lowering the potential energy barrier level, but also effectively exploits the redistributed dynamic energy and the rich MDOF dynamic characteristics introduced by the magnetically coupled linear harvester. It is found that in addition to the enhanced power harvesting performance of bistable harvester with adaptive potential, the power captured from the redistributed energy and the higher harmonic resonances introduced by the passive mechanism further increase the energy harvesting performance especially at the lower frequency range. Analytical, numerical, and experimental investigations reveal that strategically incorporating a linear harvester magnetically coupled to a conventional bistable harvester provides an effective and easy to implement means for enhancing broadband energy harvesting performance.

The accurate and reliable identification of damage in modern engineered structures is essential for timely corrective measures. Vibration-based damage prediction has been studied extensively by virtue of its global damage detection ability and simplicity in practical implementation. However, due to noise and damping influences, the accuracy of this method is inhibited when direct peak detection (DPD) is utilized to determine resonant frequency shifts. This research investigates an alternative method to detect frequency shifts caused by structural damage based on the utilization of strongly nonlinear bifurcation phenomena in bistable electrical circuits coupled with piezoelectric transducers integrated with the structure. It is shown that frequency shift predictions by the proposed approach are significantly less susceptible to error than DPD when realistic noise and damping levels distort the shifting resonance peaks. As implemented alongside adaptive piezoelectric circuitry with tunable inductance, the new method yields damage location and severity identification that is significantly more robust and accurate than results obtained following the DPD approach.

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.

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.