Seunghee Park

PZT-based active damage detection techniques for steel bridge components

This paper presents the results of experimental studies on piezoelectric lead-zirconate–titanate (PZT)-based active damage detection techniques for nondestructive evaluations (NDE) of steel bridge components. PZT patches offer special features suitable for real-time in situ health monitoring systems for large and complex steel structures, because they are small, light, cheap, and useful as built-in sensor systems. Both impedance and Lamb wave methods are considered for damage detection of lab-size steel bridge members. Several damage-sensitive features are extracted: root mean square deviations (RMSD) in the impedances and wavelet coefficients (WC) of Lamb waves, and the times of flight (TOF) of Lamb waves. Advanced signal processing and pattern recognition techniques such as continuous wavelet transform (CWT) and support vector machine (SVM) are used in the current system. Firstly, PZT patches were used in conjunction with the impedance and Lamb waves to detect the presence and growth of artificial cracks on a 1/8 scale model for a vertical truss member of Seongsu Bridge, Seoul, Korea, which collapsed in 1994. The RMSD in the impedances and WC of Lamb waves were found to be good damage indicators. Secondly, two PZT patches were used to detect damage on a bolt-jointed steel plate, which was simulated by removing bolts. The correlation of the Lamb wave transmission data with the damage classified by in and out of the wave path was investigated by using the TOF and WC obtained from the Lamb wave signals. The SVM was implemented to enhance the damage identification capability of the current system. The results from the experiments showed the validity of the proposed methods.

Electro-Mechanical Impedance-Based Wireless Structural Health Monitoring Using PCA-Data Compression and k -means Clustering Algorithms

This article presents a practical method for an electro-mechanical impedance-based wireless structural health monitoring (SHM), which incorporates the principal component analysis (PCA)-based data compression and k-means clustering-based pattern recognition. An on-board active sensor system, which consists of a miniaturized impedance measuring chip (AD5933) and a self-sensing macro-fiber composite (MFC) patch, is utilized as a next-generation toolkit of the electromechanical impedance-based SHM system. The PCA algorithm is applied to the raw impedance data obtained from the MFC patch to enhance a local data analysis-capability of the on-board active sensor system, maintaining the essential vibration characteristics and eliminating the unwanted noises through the data compression. Then, the root-mean square-deviation (RMSD)-based damage detection result using the PCA-compressed impedances is compared with the result obtained from the raw impedance data without the PCA preprocessing. Furthermore, the k-means clustering-based unsupervised pattern recognition, employing only two principal components, is implemented. The effectiveness of the proposed methods for a practical use of the electromechanical impedance-based wireless SHM is verified through an experimental study consisting of inspecting loose bolts in a bolt-jointed aluminum structure.

Automated Impedance-based Structural Health Monitoring Incorporating Effective Frequency Shift for Compensating Temperature Effects:

This study presents an impedance-based structural health monitoring (SHM) technique considering temperature effects. The temperature variation results in significant impedance variations, particularly a frequency shift in the impedance, which may lead to erroneous diagnostic results of real structures, such as civil, mechanical, and aerospace structures. In order to minimize the effect of the temperature variation on the impedance measurements, a previously proposed temperature compensation technique based on the cross-correlation between the reference-impedance data and a concurrent impedance data is revisited. In this study, cross-correlation coefficient (CC) after an effective frequency shift (EFS), which is defined as the frequency shift causing two impedance data to have the maximum correlation, is utilized. To promote a practical use of the proposed SHM strategy, an automated continuous monitoring framework using MATLAB® is developed and incorporated with the current hardware system. Validation of the proposed technique is carried out on a lab-sized steel truss bridge member under a temperature varying environment. It has been found that the CC values have shown significant fluctuations due to the temperature variation, even after applying the EFS method. Therefore, an outlier analysis providing the optimal decision limits under the inevitable variations has been carried out for more systematic damage detection. It has been found that the threshold level shall be properly selected considering the daily temperature range and the minimum target damage level for detection. It has been demonstrated that the proposed strategy combining the EFS and the outlier analysis can be effectively used in the automated continuous SHM of critical structural members under temperature variations.

Sensor Self-diagnosis Using a Modified Impedance Model for Active Sensing-based Structural Health Monitoring

The active sensing methods using piezoelectric materials have been extensively investigated for the efficient use in structural health monitoring (SHM) applications. Relying on high frequency structural excitations, the methods showed the extreme sensitivity to minor defects in a structure. Recently, a sensor self-diagnostic procedure that performs in situ monitoring of the operational status of piezoelectric (PZT) active sensors and actuators in SHM applications has been proposed. In this investigation, previously developed impedance models were revisited in order to investigate the effects of sensor and/or bonding defects on the admittance measurement. New parameters for sensor quality assessment of a PZT and coupling degradation effects between a PZT and bonding layer were incorporated into the traditional electromechanical impedance model for better estimation of the electromechanical impedance signatures and sensor diagnostics. The feasibility of the modified impedance model for sensor self-diagnosis using the admittance measurements was demonstrated by a series of parametric studies using a simple example of PZT-driven single degree of freedom spring-mass-damper system. This paper summarizes the description of the proposed modified electromechanical impedance model, parametric studies for impedance-based sensor diagnostics, and several issues that can be used as a guideline for future investigation.

Wireless impedance sensor nodes for functions of structural damage identification and sensor self-diagnosis

Economic and reliable online health monitoring strategies are very essential for safe operation of civil, mechanical and aerospace structures. This study presents online structural health monitoring (SHM) techniques using wireless impedance sensor nodes equipped with both functions of structural damage identification and sensor self-diagnosis. The wireless impedance sensor node incorporating a miniaturized impedance measuring chip, a microcontroller and radio-frequency (RF) telemetry is equipped with the capabilities for temperature sensing, multiplexing of several sensors, and local data analysis. The feasibility of the sensor node for structural damage identification is firstly investigated through a series of experimental studies inspecting loosened bolt damage and cut damage cases. Additionally, a temperature effects-free sensor self-diagnosis algorithm is embedded into the sensor node and its feasibility is examined from the experiments monitoring the integrity of each piezoelectric sensor on a wireless sensor network.

MFC-Based Structural Health Monitoring Using a Miniaturized Impedance Measuring Chip for Corrosion Detection

This article presents an experimental study using an active sensing device that consists of a miniaturized impedance-measuring chip (AD5933) and a self-sensing macrofiber composite (MFC) patch to detect corrosion in aluminum structures widely used for aerospace, civil, and mechanical systems. A simple beam structure made from a 6063 T5 aluminum alloy was selected for corrosion-detection testing. Four different corrosion cases with two different locations and two different degrees at each location were artificially inflicted on the beam using hydrochloric (HCI) acid. To identify the degrees and locations of the corrosion, the electromechanical impedance-based damage-detection technique using the proposed active sensing device was investigated. Root-mean-square deviation (RMSD) metric of the real part of the impedances obtained from the MFC patch was selected as a damage-sensitive feature. Experimental results have verified that the proposed approach can be an effective tool for detection and quantification of corrosion in aluminum structures.

A Built-in Active Sensing System-Based Structural Health Monitoring Technique Using Statistical Pattern Recognition

A piezoelectric sensor-based health monitoring technique using a two-step support vector machine (SVM) classifier is developed for railroad track damage identification. Abuilt-in active sensing system composed of two PZT patches was investigated in conjunction with both impedance and guided wave propagation methods to detect two kinds of damage in a railroad track (hole-damage 0.5cm in diameter at the web section and transverse cut damage 7.5cm in length and 0.5cm in depth at the head section). Two damage-sensitive features were separately extracted from each method: a) feature I: root mean square deviations (RMSD) of impedance signatures, and b) feature II: sum of square of wavelet coefficients for maximum energy mode of guided waves. By defining damage indices from these two damagesensitive features, a two-dimensional damage feature (2-D DF) space was made. In order to enhance the damage identification capability of the current active sensing system, a two-step SVM classifier was applied to the 2-D DF space. As a result, optimal separable hyper-planes (OSH) were successfully established by the two-step SVM classifier: Damage detection was accomplished by the first step-SVM, and damage classification was carried out by the second step-SVM. Finally, the applicability of the proposed two-step SVM classifier has been verified by thirty test patterns prepared in advance from the intact state and two damage states.

Impedance-based Damage Detection for Civil Infrastructures

The objective of this study is to investigate the feasibility of an impedance-based damage detection technique using piezoelectric (PZT) transducers for civil infrastructures such as steel bridges. The basic concept of the technique is to monitor the changes in the electrical impedance to detect structural damages. Those changes in the electrical impedance are due to the electro-mechanical coupling property of piezoelectric materials and the host structure. In this study, at first, a numerical analysis was performed to understand the basics of this technique through a simple 1-D electro-mechanical system. The experimental studies on three kinds of structural members were carried out to detect the locations of cracks and loosened bolts. It was that cracks or loosened bolts near the PZT sensors could be effectively detected by monitoring the shifts of the resonant frequencies of the impedance functions.

Impedance-based structural health monitoring using neural networks for autonomous frequency range selection

The impedance-based structural health monitoring (SHM) method has come to the forefront in the SHM community due to its practical potential for real applications. In the impedance-based SHM method, the selection of optimal frequency ranges plays an important role in improving the sensitivity of damage detection, since an improper frequency range can lead to erroneous damage detection results and provide false positive damage alarms. To tackle this issue, this paper proposes an innovative technique for autonomous selection of damage-sensitive frequency ranges using artificial neural networks (ANNs). First, the impedance signals are obtained in a wide frequency band, and the signals are split into multiple sub-ranges of this wide band. Then, the predefined damage index is evaluated for each sub-range by comparing impedance signals between the intact and the concurrent cases. Here, the cross correlation coefficients (CCs) are used as the predefined damage index. The ANN is constructed and trained using all CC values at multiple frequency ranges as multi-inputs and the real damage severity as the single output for various preselected damage scenarios, so that subsequent damage estimations may be carried out by selecting the governing frequency ranges autonomously. The performance of the proposed approach has been examined via a series of experimental studies to detect loose bolts and cracks induced on real steel bridge and building structures. It is found that the proposed approach autonomously determines the damage-sensitive frequency ranges and can be used for effective evaluation of damage severity in a wide variety of damage cases in real structures.

PZT-induced Lamb waves and pattern recognitions for on-line health monitoring of jointed steel plates

This paper presents a non-destructive evaluation (NDE) technique for detecting damages on a jointed steel plate on the basis of the time of flight and wavelet coefficient, obtained from wavelet transforms of Lamb wave signals. Probabilistic neural networks (PNNs) and support vector machines (SVMs), which are tools for pattern classification problems, were applied to the damage estimation. Two kinds of damages were artificially introduced by loosening bolts located in the path of the Lamb waves and those out of the path. The damage cases were used for the establishment of the optimal decision boundaries which divide each damage class’s region from the intact class. In this study, the applicability of the PNNs and SVMs was investigated for the damages in and out of the Lamb wave path. It has been found that the present methods are very efficient in detecting the damages simulated by loose bolts on the jointed steel plate.