• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.749-754
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• 2003

In this paper, we introduce the measurement of the underwater noise with 32channel hydrophone array of Samsung CAvitation Tunnel (SCAT) and the detection technique of noise sources by using the beam-forming method. Measurement and way signal Processing under fluid flow are essential works for the underwater acoustics, especially for the detection of noise sources. As the acoustic impedance of the water is relatively high and the tunnel is an enclosed system, we have to consider the interaction between tunnel and water together with the reflection of noise in the beam-forming technique. Also, for a hydrophone array system that is fixed on one side of tunnel wall as done in SCAT is liable to suffer from some limitations in the detection of the noise sources with the array, we discuss these limitations particularly on the frequency range and spacing of noise sources.

• Structural Engineering and Mechanics
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• v.42 no.3
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• pp.425-448
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• 2012

In this paper, a new damage detection and quantification method has been presented to perform detection and quantification of structural damage under ambient vibration loadings. To extract modal properties of the structural system under ambient excitation, natural excitation technique (NExT) and eigensystem realization algorithm (ERA) are employed. Sensitivity matrices of the dynamic residual force vector have been derived and used in the parameter subset selection method to identify multiple damaged locations. In the sequel, the steady state genetic algorithm (SSGA) is used to determine quantified levels of the identified damage by minimizing errors in the modal flexibility matrix. In this study, performance of the proposed damage detection and quantification methodology is evaluated using a finite element model of a truss structure with considerations of possible experimental errors and noises. A series of numerical examples with five different damage scenarios including a challengingly small damage level demonstrates that the proposed methodology can efficaciously detect and quantify damage under noisy ambient vibrations.

• Smart Structures and Systems
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• v.19 no.4
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• pp.393-402
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• 2017

Many vibration-based global damage detection methods attempt to extract modal parameters from vibration signals as the main structural features to detect damage. The local flexibility method is one promising method that requires only the first few fundamental modes to detect not only the location but also the extent of damage. Generally, the mode shapes in the lateral degree of freedom are extracted from lateral vibration signals and then used to detect damage for a beam structure. In this study, a new approach which employs the mode shapes in the rotary degree of freedom obtained from the macro-strain vibration signals to detect damage of a beam structure is proposed. In order to facilitate the application of mode shapes in the rotary degree of freedom for beam structures, the local flexibility method is modified and utilized. The proposed rotary approach is verified by numerical and experimental studies of simply supported beams. The results illustrate potential feasibility of the proposed new idea. Compared to the method that uses lateral measurements, the proposed rotary approach seems more robust to noise in the numerical cases considered. The sensor configuration could also be more flexible and customized for a beam structure. Primarily, the proposed approach seems more sensitive to damage when the damage is close to the supports of simply supported beams.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.4
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• pp.537-543
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• 2003

Transient vibration generated by developing localized fault in gear can be used as indicators in gear fault detection. This vibration signal suffers from the background noise such as gear meshing frequency and its harmonics and broadband noise. Thus in order to extract the information about the only gear fault from the raw vibration signal measured on the gearbox this signal is processed to reduce the background noise with many kinds of signal-processing tools. However, these signal-processing tools are often very complex and time waste. Thus. in this paper. we propose a novel approach detecting the damage of gearbox and analyzing its pattern using the raw vibration signal. In order to do this, the residual signal. which consists of the sideband components of the gear meshing frequent) and its harmonics frequencies, is extracted from the raw signal by the power spectral density (PSD) to obtain the information about the fault and is used as the input data of the artificial neural network (ANN) for analysis of the pattern of gear fault. This novel approach has been very successfully applied to the damage analysis of a laboratory gearbox.

• Journal of the Korean Society of Safety
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• v.31 no.3
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• pp.28-33
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• 2016

Since defects in the wheels of railway vehicles, which occur due to wears with the rail, cause serious damage to the running device, the diagnostic monitoring system for condition-based maintenance is required to secure the driving safety. In this paper, we studied to apply a useful Cepstrum analysis to detect periodic structure in spectrum among the vibration signal processing techniques for the fault diagnosis of a rotating body such as wheel. In order to analyze in variations of train velocity, the Cepstrum analysis was performed after a domain change of the vibration signal from time domain to rotation angle domain. When domains change, it is important to use a interpolation for a uniform interval of the rotation angle. Finally, the Cepstrum analysis for wheel flat detection was verified by using the vibration signal including the disturbance resulting from the rail irregularities and the vibration of bogie components.

• Smart Structures and Systems
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• v.14 no.2
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• pp.85-104
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• 2014

Damage detection is a challenging, complex, and at the same time very important research topic in civil engineering. Identifying the location and severity of damage in a structure, as well as the global effects of local damage on the performance of the structure are fundamental elements of damage detection algorithms. Local damage detection is essential for structural health monitoring since local damages can propagate and become detrimental to the functionality of the entire structure. Existing studies present several methods which utilize sensor data, and track global changes in the structure. The challenging issue for these methods is to be sensitive enough in identifYing local damage. Autoregressive models with exogenous terms (ARX) are a popular class of modeling approaches which are the basis for a large group of local damage detection algorithms. This study presents an algorithm, called Influence-based Damage Detection Algorithm (IDDA), which is developed for identification of local damage based on regression of the vibration responses. The formulation of the algorithm and the post-processing statistical framework is presented and its performance is validated through implementation on an experimental beam-column connection which is instrumented by dense-clustered wired and wireless sensor networks. While implementing the algorithm, two different sensor networks with different sensing qualities are utilized and the results are compared. Based on the comparison of the results, the effect of sensor noise on the performance of the proposed algorithm is observed and discussed in this paper.

• Journal of Sensor Science and Technology
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• v.31 no.1
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• pp.24-30
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• 2022

Distributed fiber-optic acoustic·vibration sensing technology is becoming increasingly popular in many industrial and academic areas such as in securing large edifices, exploring underground seismic activity, monitoring oil well/reservoir, etc. Long-range perimeter intrusion detection exemplifies an application that not only detects intrusion, but also pinpoints where it happens and recognizes kinds of threats made along the perimeter where a single fiber cable was installed. In this study, we developed a distributed fiber-optic sensing device that measures a distributed acoustic·vibration signature (pattern) for intrusion detection. In addition, we demontrate the proposed deep learning algorithm and how it classifies various intrusion events. We evaluated the sensing device and deep learning algorithm in a practical testbed setup. The evaluation results confirm that the developed system is a promising intrusion detection system for long-distance and seamless recognition requirements.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.130-139
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• 2024

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.43-48
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• 2008

Gear system has been widely used in industrial applications and unexpected failures of gears are not only extremely damaging but also lead to economic losses. So, early detection of fault is important for diagnosis machine condition. And acoustic emission is an efficient non destructive testing technique for the diagnosis of machine health and is useful technique for early detection of fault because it can find low-amplitude and high-frequency signal on account of high sensibility. Therefore, in this paper, the AE signal was measured and preprocessed using envelop analysis for gearbox with misalignment between pinion and gear. And then the vibration characteristic of gear misalignment was analyzed.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.10a
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• pp.161-164
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• 2002

Optical fiber vibrations sensors (OFVSs) and extrinsic Fabry-Perot interferometer (EFPI) were used in damage monitoring of fiber-metal laminates(FML). The optical fiber vibration sensor and EFPI were applied in order to detect and evaluate the strain, damage and failure of FML. Damages in composites, such as matrix cracks, delamination and fiber breakage may occur as a result of excessive load, fatigue and low-velocity impacts. Tensile test was performed with the measurement of optical signal and acoustic emission (AE). The signals of the optical fiber vibration sensor due to damages were quantitatively evaluated by wavelet transform. EFPI was less sensible to the damage signals compared with the optical fiber vibration sensor. It was found that damage information of comparable in quality to acoustic emission data could be obtained from the optical fiber vibration sensor signals.