• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.982-986
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• 1997

A method using cutting force signal and neural network for detection tool damage is proposed. Cutting force signal is gained by tool dynamometer and the signal is prepocessed to normalize. Cutting force signal is changed by tool state. When tool damage is occurred, cutting force signal goes up in comparison with that in normal state. However,the signal goes down in case of catastrophic fracture. These features are memorized in neural network through nomalizing couse. A new nomalizing method is introduced in this paper. Fist, cutting forces are sumed up except data smaller than threshold value, which is the cutting force during non-cutting action. After then, the average value is found by dividing by the number of data. With backpropagation training process, the neural network memorizes the feature difference of cutting force signal between with and without tool damage. As a result, the cutting force can be used in monitoring the condition of cutting tool and neural network can be used to classify the cutting force signal with and without tool damage.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2001년도 춘계학술대회논문집
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• pp.583-589
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• 2001

For several decades many researchers have studied various algorithms, known as non-destructive testing, to identify abnormalities within a structure. Damage detection technique using vibration signal is a kind of these methods. Many researchers have published lots of papers dealing vibration signal to identify structural damage. All the methods for damage detection using vibration signal can be divided into two big categories. The first category is the method that requires some reference model such as finite element model, and the second is the method that does not require any reference model but needs only experimental data. This paper will be devoted to classify damage detection methods that utilize vibration signal.

• Smart Structures and Systems
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• 제22권2호
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• pp.239-247
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• 2018

A magnetic flux leakage (MFL) method was applied to detect and quantify defects in a steel bar. A multi-channel MFL sensor head was fabricated using Hall sensors and magnetization yokes with permanent magnets. The MFL sensor head scanned a damaged specimen with five levels of defects to measure the magnetic flux density. A series of signal processing procedures, including an enveloping process based on the Hilbert transform, was performed to clarify the flux leakage signal. The objective damage detection of the enveloped signals was then analyzed by comparing them to a threshold value. To quantitatively analyze the MFL signal according to the damage level, five kinds of damage indices based on the relationship between the enveloped MFL signal and the threshold value were applied. Using the proposed damage indices and the general damage index for the MFL method, the detected MFL signals were quantified and analyzed relative to the magnitude of the damage increase.

• Structural Monitoring and Maintenance
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• 제11권3호
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• pp.203-217
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• 2024

Electro-mechanical impedance (EMI) technique is a low-cost structural damage detection method. It reflects structural damage through the change in admittance signal which contains the structural mechanical impedance information. The ambient temperature greatly affects the admittance signal, which hides the changes caused by structural damage and reduces the accuracy of damage identification. This study introduces a convolutional neural network to compensate for the temperature effect. The proposed method uses a framework that consists of a feature extraction network and a decoding network, and the original admittance signal with temperature information is used as the input. The output admittance signal is eliminated from the temperature effect, improving damage identification robustness. The admittance data simulated by the finite element model of the spatial grid structure is used to verify the effectiveness of the proposed method. The results show that the proposed method has advantages in identification accuracy compared with the damage index minimization method and the principal component analysis method.

• 한국항공우주학회지
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• 제49권1호
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• pp.13-20
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• 2021

본 논문에서는 반사된 면적 결과와 반사된 위치 결과를 중첩하고, 가시화된 이미지에서 픽셀의 최대값에 대한 비율로 윤곽선을 추출하여 손상 크기를 추정하는 알고리즘을 제안하였다. 반사된 신호의 면적을 구하기 위해서 누적함수 특성벡터 알고리즘을 사용하였고, 반사된 신호의 위치를 구하기 위해서 신호 상관관계를 이용해서 측정신호로부터 손상 신호를 분리하였다. 그리고 제안한 손상크기 추정 알고리즘을 복합재 평판에서 실험 수행하여 검증하였다. 임의의 위치에서 반복 실험을 수행해서 제안된 알고리즘이 반복에 따른 재현성이 있음을 검증하였고, 손상크기 변화에 따라 손상 크기를 추정하고 분별할 수 있음을 검증하였다.

• 한국안전학회지
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• 제38권2호
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• pp.36-43
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• 2023

In this study, a four-point bending test was conducted to assess and detect the damage to reinforced concrete structures using the acoustic emission (AE) technique. Based on the crack investigation results, flexural failure was classified into four stages and compared with the characteristic analysis results of AE parameters. The parametric characterization indicated that the activity of the primary AE signal was high in the early stage, and that of the second signal increased after the flexural cracks stabilized. Because the secondary AE signal included noise generated by friction, parameter-based analysis for damage assessment was performed using the primary signal; the secondary signal was used as complement. The activity analyses of the primary and secondary signals effectively classified crack propagation; however, determining the macrocracks and yielding of reinforcing bars had certain limitations. Nevertheless, applying the damage index with cumulative AE energy is a complementary technique for detecting and assessing structure damage that well detects the occurrence of macrocracks.

• Smart Structures and Systems
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• 제1권2호
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• pp.185-215
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• 2005

Advanced signal processing techniques have been long introduced and widely used in structural health monitoring (SHM) and nondestructive evaluation (NDE). In our research, we applied several signal processing approaches for our embedded ultrasonic structural radar (EUSR) system to obtain improved damage detection results. The EUSR algorithm was developed to detect defects within a large area of a thin-plate specimen using a piezoelectric wafer active sensor (PWAS) array. In the EUSR, the discrete wavelet transform (DWT) was first applied for signal de-noising. Secondly, after constructing the EUSR data, the short-time Fourier transform (STFT) and continuous wavelet transform (CWT) were used for the time-frequency analysis. Then the results were compared thereafter. We eventually chose continuous wavelet transform to filter out from the original signal the component with the excitation signal's frequency. Third, cross correlation method and Hilbert transform were applied to A-scan signals to extract the time of flight (TOF) of the wave packets from the crack. Finally, the Hilbert transform was again applied to the EUSR data to extract the envelopes for final inspection result visualization. The EUSR system was implemented in LabVIEW. Several laboratory experiments have been conducted and have verified that, with the advanced signal processing approaches, the EUSR has enhanced damage detection ability.

• 한국철도학회논문집
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• 제14권4호
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• pp.320-326
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• 2011

본 연구에서는 Lamb파에 대한 시간-반전과정과 이미지기법을 기반으로 하여 기준 데이터를 사용하지 않는 구조물 건전성 모니터링(SHM) 기술을 제안하였다. 제안된 기술이 갖는 주요 세가지 특징은 다음과 같다: (1) 제안된 기술에서는 귀환신호를 직접 손상진단에 사용하기 때문에 귀환신호와 초기 입력신호의 차이로부터 손상신호를 구할 필요가 없다; (2) 기존의 기술에서 널리 사용되는 형상비교법을 사용하지 않고 귀환신호에서 얻는 비시간 정보를 활용하는 이미지기법을 사용하였다; (3) 손상 이미지를 보다 뚜렷하게 얻기 위하여 이미지에 대한 개선된 수학적 정의를 사용하였다. 본 연구에서 제안한 SHM기술은 손상을 평판의 몇몇 위치에 부가한 경우에 대한 손상탐지 실험을 수행함으로써 검증하였다.

• Structural Engineering and Mechanics
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• 제71권1호
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• pp.57-63
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• 2019

During the last two decades, much joint research regarding vibration based methods has been done, leading to developing various algorithms and techniques. These algorithms and techniques can be divided into modal methods and signal methods. Although modal methods have been widely used for health monitoring and damage detection, signal methods due to higher efficiency have received considerable attention in various fields, including aerospace, mechanical and civil engineering. Signal-based methods are derived directly from the recorded responses through signal processing algorithms to detect damage. According to different signal processing techniques, signal-based methods can be divided into three categories including time domain methods, frequency domain methods, and time-frequency domain methods. The frequency domain methods are well-known and interest in using them has increased in recent years. To determine dynamic behaviours, to identify systems and to detect damages of bridges, different methods and algorithms have been proposed by researchers. In this study, a new algorithm to detect seismic damage in the bridge's piers is suggested. To evaluate the algorithm, an analytical model of a bridge with simple spans is used. Based on the algorithm, before and after damage, the bridge is excited by a sine force, and the piers' responses are measured. The dynamic specifications of the bridge are extracted by Power Spectral Density function. In addition, the Least Square Method is used to detect damage in the bridge's piers. The results indicate that the proposed algorithm can identify the seismic damage effectively. The algorithm is output-only method and measuring the excitation force is not needed. Moreover, the proposed approach does not need numerical models.

• 비파괴검사학회지
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• 제27권6호
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• pp.541-549
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• 2007

This work describes an investigation into the feasibility of using an acoustic emission (AE) technique to evaluate the integrity of a composite actuator with a PZT ceramic under electromechanical cyclic loading. AE characteristics have been analyzed in terms of the behavior of the AE count rate and signal waveform in association with the performance degradation of the composite actuator during the cyclic tests. The results showed that the fatigue cracking of the composite actuator with a PZT ceramic occurred only in the PZT ceramic layer, and that the performance degradation caused by the fatigue damage varied immensely depending on the existence of a protecting composite bottom layer. We confirmed the correlations between the fatigue damage mechanisms and AE signal types for the actuators that exhibited multiple modes of fatigue damage; transgranular micro damage, intergranular fatigue cracking, and breakdown by a short circuiting were related to a burst type signal showing a shortly rising and slowly decaying waveform with a comparably low voltage, a continuous type signal showing a gradual rising and slowly decaying waveform with a very high voltage and a burst and continuous type signal with a high voltage, respectively. Results from the present work showed that the evolution of fatigue damage in the composite actuator with a PZT ceramic can be nondestructively identified via in situ AE monitoring and microscopic observations.