• Smart Structures and Systems
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• v.22 no.2
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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.

• Wind and Structures
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• v.17 no.6
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• pp.609-627
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• 2013

The Yingxian wooden tower in China is currently the tallest wooden tower in the world. It was built in 1056 AD and is 65.86 m high. Field measurements of wind speed and wind-induced response of this tower are conducted. The wind characteristics, including the average wind speed, wind direction, turbulence intensity, gust factor, turbulence integral length scale and velocity spectrum are investigated. The power spectral density and the root-mean-square wind-induced acceleration are analyzed. The structural modal parameters of this tower are identified with two different methods, including the Empirical Mode Decomposition (EMD) combined with the Random Decrement Technique (RDT) and Hilbert transform technique, and the stochastic subspace identification (SSI) method. Results show that strong wind is coming predominantly from the West-South of the tower which is in the same direction as the inclination of the structure. The Von Karman spectrum can describe the spectrum of wind speed well. Wind-induced torsional vibration obviously occurs in this tower. The natural frequencies identified by EMD, RDT and Hilbert Transform are close to those identified by SSI method, but there is obvious difference between the identified damping ratios for the first two modes.

• Journal of KSNVE
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• v.3 no.4
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• pp.341-352
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• 1993

Although a frequency analysis by FFT algorithm has been widely used in the vibration community, this approach has somewhat limited features when an analysist want to see the details of frequency trends because FFT shows only energy contents along frequencies. So the concept of instantaneous frequency that represents the dominant frequency component at each time needs to be introduced. In this paper, to get the instantaneous frequency, two methods are used. Methods using Hilbert transform and evolutionary spectrum are those. One of the problems of estimating instantaneous frequency using Hilbert transform is that it is normally very sensitive to signal to noise ratio(SNR) because of the differentiation. Moving window is applied on the estimation of instantaneous frequency, and instantaneous frequency histogram are used to handle this problem and proved to be very effective. Computer simulations for various signals have been done to understand the characteristics of instantaneous frequency. The usefulness of signal analysis using instantaneous frequency was tested by three simple experiments, which were engine experiment, beam experiment, and car door experiment. The instantaneous frequency analysis is found to be a useful technique to analyze the signals that have time varying frequencies.

• Journal of the Korean Society of Propulsion Engineers
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• v.11 no.2
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• pp.62-70
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• 2007

The aiming ability is a to improve accuracy performance of the firing vehicle. This paper describes the detection method of chatter vibration using disturbance acceleration in the pointing structure. In order to analysis vibration trends of the pointing system occurred during vehicle drive, acceleration data was processed by using data processing algorithm with moving average and Hilbert transform. Specific mode constants of acceleration were obtained under various disturbances. Vehicle velocity, road condition, property of pointing structure were considered as factors which make change of vibration trend in vehicle dynamics. Finally, back propagation neural networks have been applied to the pattern recognition for the classification of vibration signal in various driving conditions. Results of signal processing were compared and analysed.

• 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.

• Journal of Ship and Ocean Technology
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• v.8 no.3
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• pp.18-25
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• 2004

Sound reception system is required to detect the sound and the quadrantal direction of the other ship's horn sound, to overcome the effects of enclosed wall for navigation space, functioning as a sound barrier. However, the realized systems can only provide quadrantal information of the other ship. This paper presents a new arrangement of microphones, having geometrically symmetric deployment with the same distances between sensors and the same angles between adjacent sensors with respect to the geometrical center. The sound pressures received at microphones are transformed into the related envelope signals by applying Hilbert transform. The time delays between microphones are estimated by the correlation functions between the derived envelope signals. This envelope base processing mitigates the noises related to the reflection by ship and sea surface. Then, the directional information is easily defined by using the estimated time delays. The suggested method is verified by the generated signals using boundary element method for a small ship model with sea surface wave. The estimated direction is quite similar to the true one and therefore the proposed approach can be used as an efficient sound reception system.

• Journal of the Society of Naval Architects of Korea
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• v.53 no.6
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• pp.482-493
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• 2016

The identification of modal damping of a segmented hull model with torsional response is difficult task due to the coupling of modal response. This is because the 1st and 2nd torsional vibration modes are closely spaced in frequency domain leading to the situation that the modal decomposition is difficult to achieve by simple band-pass filter. Present study applied several different modal decomposition methods to derive the damping ratio of different modes. The modal decomposition methods considered in this study are simple band-pass filter, Hilbert vibration decomposition, Wavelet transform and proper orthogonal decomposition. Coupled free decay signal obtained from the torsional hammering test on a segmented hull model was processed with four different methods and the derived damping ratios were compared with each other. Discussions also have been made on the pros and cons of the different methodologies.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.284-287
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• 2003

A wavelet transform which is presented as a new technique of signal processing field decompose input signals into subsignals for expressing them in different resolutions and into detail signals for expressing the remaining signals. And the signals obtained from the progress include the information about input signals at the same time and scale. And when two wavelet bases are designed to form Hilbert transform pair, wavelet Pair show superior performance than the existing DWT in data detection of pulse type. Therefore in this paper, we detected position of impulse noise by using two dyadic wavelet bases which are designed by truncated coefficient vector.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.2
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• pp.29-36
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• 2009

The analysis of Radar signal, telecommunication, bioengineering, seismic, and acoustic signal is consist of the Non-stationary signal which has non-linear phase variation. Non-stationary signal means that the physical properties of signal depend on time variation and the instantaneous frequency represents physical property of these type of signal. Thus estimation of the instantaneous frequency of non-stationary signal is important subject in signal processing. In this work, the instantaneous frequency analysis method utilizing continuous wavelets transform is represented and compared with Hilbert Transform method.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.7
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• pp.555-561
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• 2014

Acoustic Emission technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the bearing problems and wavelet transform is a powerful method to detect faults occurred on rotating machinery. However, exact method for AE signal is not developed yet for the rotating machinery diagnosis. Therefore, in this paper two methods which are processed by Hilbert transform and DET for feature extraction. In addition, we evaluate the classification performance with varying the parameter from 2 to 15 for feature selection DET, 0.01 to 1.0 for the RBF kernel function of SVR, and the proposed algorithm achieved 94 % classification of averaged accuracy with the parameter of the RBF 0.08, 12 feature selection.