• Smart Structures and Systems
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• v.3 no.2
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• pp.201-217
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• 2007

An active damage detection technique is introduced to locate damage in an isotropic plate using Lamb waves. This technique uses a time-domain energy model of Lamb waves in plates that the wave amplitude inversely decays with the propagation distance along a ray direction. Accordingly the damage localization is formulated as a least-squares problem to minimize an error function between the model and the measured data. An active sensing system with integrated actuators/sensors is controlled to excite/receive $A_0$ mode of Lamb waves in the plate. Scattered wave signals from the damage can be obtained by subtracting the baseline signal of the undamaged plate from the recorded signal of the damaged plate. In the experimental study, after collecting the scattered wave signals, a discrete wavelet transform (DWT) is employed to extract the first scattered wave pack from the damage, then an iterative method is derived to solve the least-squares problem for locating the damage. Since this method does not rely on time-of-flight but wave energy measurement, it is more robust, reliable, and noise-tolerant. Both numerical and experimental examples are performed to verify the efficiency and accuracy of the method, and the results demonstrate that the estimated damage position stably converges to the targeted damage.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.7
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• pp.129-136
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• 2019

In this study, the magnetic flux leakage technique was applied to diagnose steel plate damage, imaging technique was applied through those signals. Steel plate specimens with different thicknesses were prepared for the imaging the magnetic flux leakage signal, and 6 different depths of damage were artificially processed at the same locations on each specimen. The sensor head consist hall sensor and magnetization yoke was fabricated to magnetize the steel plate specimen and measure the magnetic flux leakage signal. In order to remove the noise and increase the resolution of the image in the signal collected from the hall sensor, various of signal processing was performed. P-P value was analyzed for each channel to analyze the magnetic flux leakage signals measured from each damaged part. Based on the above processed signals and analysis, it was converted into heatmap image. Through this, it was possible to identify the damage on the steel plate at glance by imaging magnetic flux leakage signal.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.7
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• pp.43-50
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• 2004

For the simultaneous measurement of strain and damage signal a fiber Bragg grating sensor system with a dual demodulator was proposed. The dual demodulator is composed of a demodulator using a tunable Fabry-Perot filter measuring the low-frequency signal with large magnitude such as strain and the other using a passive Mach-Zehnder interferometer detecting the high-frequency signal with small amplitude such as impact or damage signal. Using the proposed fiber Bragg grating sensor system, both the strain and damage signals of a cross-ply laminated composite beam under tensile loading were simultaneously measured. The strain and damage signals detected by single fiber Bragg grating sensor showed that sudden strain shifts were accompanied with vibration at a maximum frequency of several hundreds of kilohertz at the instant of matrix crack propagation in the 90 degree layer in composite beam.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.251-258
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• 2013

We studied it about ways to detect damage of a tire about randomness ultrasonic signal which occurs repeatedly while rub a tire of driving car and a road surface. The signal randomness is decreased through the preprocess of short-time energy calculation and the average value of coherence function is used by the normalization expression of the signal randomness. The process limit that can be decide on the dominant period of a signal using the coherence threshold is analyzed and the algorithm to decide the dominant period is proposed by setting up the -3dB threshold of the maximum value on the power spectrum.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2005.04a
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• pp.235-239
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• 2005

In this research, we investigated the frequency characteristic of low-velocity impact induced damage signals on graphite/epoxy composite laminates using high-speed fiber Bragg grating(FBG) sensor system. Appling the FBG sensors to damage assessment, we need to study the response of FBG sensors as the damage signals of the different incident angles because FBG shows different directional sensitivity. In order to discriminate an impact induced damage signal from that of undamaged case, drop impacts with different energies were applied to the composite panel with different incident angle to the FBG sensor. Finally, detected impact signals were compared using frequency distributions of wavelet detail components in order to find distinctive signal characteristics of composites delamination.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.4
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• pp.269-282
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• 2009

This paper presents a novel approach for Lamb wave based structural health monitoring(SHM) in honeycomb aluminum panels. In this study, a suite of three signal processing algorithms are employed to improve the damage detection capability. The signal processing algorithms used include wavelet attenuation, correlation coefficients of power density spectra, and triangulation of reflected waves. Piezoelectric transducers are utilized as both sensors and actuators for Lamb wave propagation. These SHM algorithms are built into a MatLab interface that integrates and automates the hardware and software operations and displays the results for each algorithm to the analyst for side by side comparison. The effectiveness of each of these signal processing algorithms for SHM in honeycomb aluminum panels under a variety of damage conditions is then demonstrated.

• Smart Structures and Systems
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• v.5 no.4
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• pp.457-468
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• 2009

A novel approach for integrating active sensing data interrogation algorithms for structural health monitoring (SHM) applications is presented. These algorithms cover Lamb wave propagation, impedance methods, and sensor diagnostics. Contrary to most active-sensing SHM techniques, which utilize only a single signal processing method for damage identification, a suite of signal processing algorithms are employed and grouped into one package to improve the damage detection capability. A MATLAB-based user interface, referred to as HOPS, was created, which allows the analyst to configure the data acquisition system and display the results from each damage identification algorithm for side-by-side comparison. By grouping a suite of algorithms into one package, this study contributes to and enhances the visibility and interpretation of the active-sensing methods related to damage identification. This paper will discuss the detailed descriptions of the damage identification techniques employed in this software and outline future issues to realize the full potential of this software.

• Smart Structures and Systems
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• v.16 no.1
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• pp.67-79
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• 2015

Acoustic emission analysis is an effective technique for monitoring the evolution of damage in a structure. An experimental analysis on a set of reinforced concrete beams under flexural loading was carried out. A mixed AE analysis method which used both parameter-based and signal-based techniques was presented to characterize and identify different failure mechanisms of damage, where the signal-based analysis was performed by using the Hilbert-Huang transform. The maximum instantaneous energy of typical damage events and the corresponding frequency characteristics were established, which provided a quantitative assessment of reinforced concrete beam using AE technique. In the bending tests, a "pitch-catch" system was mounted on a steel bar to assess bonding state of the steel bar in concrete. To better understand the AE behavior of bond-slip damage between steel bar and concrete, a special bond-slip test called pullout test was also performed. The results provided the basis of quantitative AE to identify both failure mechanisms and level of damages of civil engineering structures.

• Structural Monitoring and Maintenance
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• v.8 no.4
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• pp.379-402
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• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.6
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• pp.67-73
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• 2022

Industrial inverters are used in a variety of fields for electric power supply. They may be exposed to vibration and heat once they are installed. This study focused on a framework of accelerated life testing of an industrial inverter considering fatigue damage as the primary source of deterioration. Instead of analyzing detailed failure mechanisms and the product's vulnerability to them, the potential of fatigue failure is considered using the fatigue damage spectrum calculated from the environmental vibration signals. The acceleration and temperature data were gathered using field measurement and spectral analysis was conducted to calculate the vibration signal's power spectral density (PSD). The fatigue damage spectrum is then calculated from the input PSD data and is used to design an accelerated fatigue life testing. The PSD for the shaker table test is derived that has the equivalent fatigue damage to the original input signal. The tests were performed considering the combined effect of random vibration and elevated temperature, and the product passed all the planned tests. It was successfully demonstrated that the inverter used in this study could survive environmental vibration up to its guarantee period. The fatigue damage spectrum can effectively be used to design accelerated fatigue life testing.