Seung-Seop Jin

A performance-enhanced energy harvester for low frequency vibration utilizing a corrugated cantilevered beam

This note proposes a performance-enhanced piezoelectric energy harvester by replacing a conventional flat cantilevered beam with a corrugated beam. It consists of a proof mass and a sinusoidally or trapezoidally corrugated cantilevered beam covered by a polyvinylidene fluoride (PVDF) film. Compared to the conventional energy harvester of the same size, it has a more flexible bending stiffness and a larger bonding area of the PVDF layer, so higher output voltage from the device can be expected. In order to investigate the characteristics of the proposed energy harvester, analytical developments and numerical simulations on its natural frequency and tip displacement are carried out. Shaking table tests are also conducted to verify the performance of the proposed device. It is clearly shown from the tests that the proposed energy harvester not only has a lower natural frequency than an equivalent sized standard energy harvester, but also generates much higher output voltage than the standard one.

Vibration-based structural health monitoring using adaptive statistical method under varying environmental condition

It is well known that the dynamic properties of a structure such as natural frequencies depend not only on damage but also on environmental condition (e.g., temperature). The variation in dynamic characteristics of a structure due to environmental condition may mask damage of the structure. Without taking the change of environmental condition into account, false-positive or false-negative damage diagnosis may occur so that structural health monitoring becomes unreliable. In order to address this problem, an approach to construct a regression model based on structural responses considering environmental factors has been usually used by many researchers. The key to success of this approach is the formulation between the input and output variables of the regression model to take into account the environmental variations. However, it is quite challenging to determine proper environmental variables and measurement locations in advance for fully representing the relationship between the structural responses and the environmental variations. One alternative (i.e., novelty detection) is to remove the variations caused by environmental factors from the structural responses by using multivariate statistical analysis (e.g., principal component analysis (PCA), factor analysis, etc.). The success of this method is deeply depending on the accuracy of the description of normal condition. Generally, there is no prior information on normal condition during data acquisition, so that the normal condition is determined by subjective perspective with human-intervention. The proposed method is a novel adaptive multivariate statistical analysis for monitoring of structural damage detection under environmental change. One advantage of this method is the ability of a generative learning to capture the intrinsic characteristics of the normal condition. The proposed method is tested on numerically simulated data for a range of noise in measurement under environmental variation. A comparative study with conventional methods (i.e., fixed reference scheme) demonstrates the superior performance of the proposed method for structural damage detection.

Vibration-based damage detection using online learning algorithm for output-only structural health monitoring:

Damage-sensitive features such as natural frequencies are widely used for structural health monitoring; however, they are also influenced by the environmental condition. To address the environmental effect, principal component analysis is widely used. Before performing principal component analysis, the training data should be defined for the normal condition (baseline model) under environmental variability. It is worth noting that the natural change of the normal condition may exist due to an intrinsic behavior of the structural system. Without accounting for the natural change of the normal condition, numerous false alarms occur. However, the natural change of the normal condition cannot be known in advance. Although the description of the normal condition has a significant influence on the monitoring performance, it has received much less attention. To capture the natural change of the normal condition and detect the damage simultaneously, an adaptive statistical process monitoring using online learning algorithm is proposed for output-only structural health monitoring. The novelty aspect of the proposed method is the adaptive learning capability by moving the window of the recent samples (from normal condition) to update the baseline model. In this way, the baseline model can reflect the natural change of the normal condition in environmental variability. To handle both change rate of the normal condition and non-linear dependency of the damage-sensitive features, a variable moving window strategy is also proposed. The variable moving window strategy is the block-wise linearization method using k-means clustering based on Linde–Buzo–Gray algorithm and Bayesian information criterion. The proposed method and two existing methods (static linear principal component analysis and incremental linear principal component analysis) were applied to a full-scale bridge structure, which was artificially damaged at the end of the long-term monitoring. Among the three methods, the proposed method is the only successful method to deal with the non-linear dependency among features and detect the structural damage timely.

Likelihood-free Bayesian computation for structural model calibration: a feasibility study

Finite element (FE) model updating is often used to associate FE models with corresponding existing structures for the condition assessment. FE model updating is an inverse problem and prone to be ill-posed and ill-conditioning when there are many errors and uncertainties in both an FE model and its corresponding measurements. In this case, it is important to quantify these uncertainties properly. Bayesian FE model updating is one of the well-known methods to quantify parameter uncertainty by updating our prior belief on the parameters with the available measurements. In Bayesian inference, likelihood plays a central role in summarizing the overall residuals between model predictions and corresponding measurements. Therefore, likelihood should be carefully chosen to reflect the characteristics of the residuals. It is generally known that very little or no information is available regarding the statistical characteristics of the residuals. In most cases, the likelihood is assumed to be the independent identically distributed Gaussian distribution with the zero mean and constant variance. However, this assumption may cause biased and over/underestimated estimates of parameters, so that the uncertainty quantification and prediction are questionable. To alleviate the potential misuse of the inadequate likelihood, this study introduced approximate Bayesian computation (i.e., likelihood-free Bayesian inference), which relaxes the need for an explicit likelihood by analyzing the behavior similarities between model predictions and measurements. We performed FE model updating based on likelihood-free Markov chain Monte Carlo (MCMC) without using the likelihood. Based on the result of the numerical study, we observed that the likelihood-free Bayesian computation can quantify the updating parameters correctly and its predictive capability for the measurements, not used in calibrated, is also secured.

Performance enhancement of piezoelectric energy harvesting system using a corrugated cantilever beam

In this paper, a piezoelectric energy harvesting device consisting of a proof mass and a corrugated cantilever beam is proposed in order to enhance its performance (i.e., an increase in output voltage as well as a reduction in resonant frequency). The sinusoidal or trapezoidal shape of a cantilever beam is able to make the bonding area of piezoelectric materials (e.g., polyvinylidene fluoride (PVDF) film) much larger, resulting in higher output voltages. Moreover, the natural frequency of the device can be significantly decreased due to low flexural rigidity of the beam member. This lownatural frequency device would fit well for civil engineering applications because most civil structures such as bridges and buildings have low natural frequencies. In order to examine the geometrical characteristics of the proposed device, an analytical development and a numerical simulation are carried out. Besides, shaking table tests are conducted with a prototype of energy harvesting device. It is demonstrated from numerical and experimental studies that the proposed energy harvester can shift down its resonant frequency considerably and generate much higher output power as compared with a conventional one having a flat (or straight) cantilever beam.

Finite element model updating of a PSC box girder bridge using ambient vibration test

In this paper, in-field ambient vibration testing of a highway bridge in South Korea under traffic loadings has been conducted to update its finite element model for future predictive analysis and diagnosis purpose. The research results presented in this paper are outcomes from an international REU (Research Experience for Undergraduates) program in smart structures funded by US-NSF (National Science Foundation) and hosted abroad by the Korean Advanced Institute of Science and Technology (KAIST). The monitoring, modeling, and model updating of civil infrastructures are vital in maintaining new design and maintenance standards. Using the frequency domain decomposition (FDD), experimental modal properties of the structure were found and, after a finite element model was created and updated based on the modal properties. From the results, it has been concluded that (a) the FDD method successfully identified the modal characteristics of the structure from ambient vibration, (b) that model updating improved the accuracy of the finite element model, (c) Representing the structural supports as springs in the FEM improved the results from the ideally supported model.