• The KSFM Journal of Fluid Machinery
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• v.7 no.2 s.23
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• pp.41-49
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• 2004

Condition monitoring is very important in turbo machinery because single failure could cause critical damages to its plant. So, automatic fault recognition has been one of the main research topics in condition monitoring area. We have used a relatively new fault recognition method, Hidden Markov Model(HMM), for mechanical system. It has been widely used in speech recognition, however, its application to fault recognition of mechanical signal has been very limited despite its good potential. In this paper, discrete HMM(DHMM) was used to recognize the faults of rotor system to study its fault recognition ability. We set up a rotor kit under unbalance and oil whirl conditions and sampled vibration signals of two failure conditions. DHMMS of each failure condition were trained using sampled signals. Next, we changed the setup and the rotating speed of the rotor kit. We sampled vibration signals and each DHMM was applied to these sampled data. It was found that DHMMs trained by data of one rotating speed have shown good fault recognition ability in spite of lack of training data, but DHMMs trained by data of four different rotating speeds have shown better robustness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.431-437
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• 2013

These days, many people live a healthy life, but suffering caused by chronic diseases. The main factors of chronic diseases are stress, blood pressure and obesity. Chronic diseases which are caused by high blood pressure are very high incidence. Therefore, this paper suggests the ways to prevent as diagnosis a phenomenon that occur rising in blood pressure consistently by analyzing the voice according th rising in blood pressure. For this, I studied some influence on voicing through rising in blood pressure by applying pitch that measure vocal fold vibration and intensity that measure voice energy size that is one of technique. That collect and analyse the voice after rising blood pressure by aerobic exercise.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.2
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• pp.185-190
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• 2010

Condition monitoring(CM) is a method based on non-destructive test(NDT). So, recently many kind of NDT were applied for CM. Acoustic emission(AE) is widely used for the early detection of faults in rotating machinery in these days because of high sensitivity than common accelerometers and detectable low energy vibration signals. And crack is considered one of severe fault in the rotating machine. Therefore, in this paper, study on early detection using AE has been accomplished for the crack of the low-speed shaft. There is a seeded initial crack on the shaft then the AE signal had been measured with low-speed rotation as the applied load condition. The signal detected from crack in rotating machine was detected by the AE transducer then the trend of crack growth had found out by using some of feature values such as peak value, skewness, kurtosis, crest factor, frequency center value(FC), variance frequency value(VF) and so on.

• Journal of KSNVE
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• v.6 no.1
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• pp.71-82
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• 1996

The finite element analysis for rotor bearing systems has been an essential tool for design, identification, and diagnosis of rotating machinery. Among others, the unbalance response analysis is fundamental in the vibration analysis of rotor bearing systems because rotating unbalance is recognized as a common sourve of vibration in rotating machinery. However there still remains a problem in the aspect of computational efficiency for unbalance response analysis of large rotor bearing systems. Gyroscopic terms and local bearing parameters in rotor bearing systems often make matters worse in unbalance response computation due to the complicated dynamic properties such as rotational speed dependency and/or anisotropy. The present paper proposes an efficient method for unbalance responses of multi-span rotor bearing systems. An improved substructure synthesis scheme is introduced which makes it possible to compute unbalance responses of the system by coupling unbalance responses of substructures that are of self adjoint problem with small order matrices. The present paper also suggests a scheme to easily deal with gyroscopic tems and local, coupling or bearing parameters. The proposed method causes no errors even though the computational effort is reduced drastically. The present method is demonstrated through three test examples.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2001.11a
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• pp.43-52
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• 2001

Condition monitoring plays a vital role since it sustains the reliable operation of industrial plant and machinery in the pursuit of economic whole life operation. In order to achieve this goal, it is needed to monitor various parameters of mechanical system such as vibration, wear, temperature, and etc., and finally to diagnosis the root causes of any possible abnormal machine condition. In this work, we constructed a rotor system where various types of functional machine failures occurred frequently in industry were induced. Characteristics of the machine failure were monitored simultaneously by the on-line measurement of vibration, wear and temperature. Result showed that these parameters responded differently to the induced functional machine failure. The availability of each parameter on effective condition monitoring was discussed in this work.

• Smart Structures and Systems
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• v.33 no.3
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• pp.227-241
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• 2024

Safety and structural integrity of civil structures, like bridges and buildings, can be substantially enhanced by employing appropriate structural health monitoring (SHM) techniques for timely diagnosis of incipient damages. The information gathered from health monitoring of important infrastructure helps in making informed decisions on their maintenance. This ensures smooth, uninterrupted operation of the civil infrastructure and also cuts down the overall maintenance cost. With an early warning system, SHM can protect human life during major structural failures. A real-time online damage localization technique is proposed using only the vibration measurements in this paper. The concept of the 'Degree of Scatter' (DoS) of the vibration measurements is used to generate a spatial profile, and fractal dimension theory is used for damage detection and localization in the proposed two-phase algorithm. Further, it ensures robustness against environmental and operational variability (EoV). The proposed method works only with output-only responses and does not require correlated finite element models. Investigations are carried out to test the presented algorithm, using the synthetic data generated from a simply supported beam, a 25-storey shear building model, and also experimental data obtained from the lab-level experiments on a steel I-beam and a ten-storey framed structure. The investigations suggest that the proposed damage localization algorithm is capable of isolating the influence of the confounding factors associated with EoV while detecting and localizing damage even with noisy measurements.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.86-92
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• 2009

As increasing of using induction motors, the induction motors faults cause serious damage to the industry. Therefore to find out faults of induction motor is recognized as important problem awaiting solution. But to make matters worse, the faults of induction motors often progress through long time. It means that early diagnosis is very important. Many researches have been progressed and general method of diagnosis is using vibration sensor to diagnose fault of induction motor. However, although it is reliability technique, it demands high price and it is difficult to use. This paper presents an implementation of technique for fault diagnosis of induction motor using wavelet transform based stator current and it is composed with algorithm that decides whether fault existence or not using C++ based on windows software. The algorithm will be accomplished in real-time using current data acquisition board and PC automatically with Neural Network algorithm.

• The Journal of the Acoustical Society of Korea
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• v.39 no.4
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• pp.263-269
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• 2020

In this study, a rotating machine that can reproduce normal condition and 8 fault conditions were produced, and vibration data was acquired. Feature is calculated from the acquired data, and accuracy is analyzed through fault diagnosis using artificial neural networks and genetic algorithms. In order to achieve optimal timing and higher accuracy, features by three domains were applied to the fault diagnosis. The learning number was selected as a setting variable. As a result of the rotating machine fault diagnosis, high precision was found in the frequency domain than in others, and precise fault diagnoses were accomplished through all of 10 operations, at the learning number of 5000 and 8000. Given the efficiency of time, it was estimated to be the most efficient when the number of learning was 5000.

• Journal of Oral Medicine and Pain
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• v.24 no.1
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• pp.37-47
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• 1999

This study was performed to investigate the factors related to vibration of temporomandibular joint during mandibular opening movement. For this study, 144 patients with temporomandibular disorders were randomly selected. Angle's classification, lateral guidance pattern, range of maximal mouth opening, preferred chewing side, and affected side were investigated clinically. Mandibular torque rotational movement during opening was recorded with $BioEGN^{(R)}$ and vibration of temporomandibular joint during opening was recorded with $Sonopak^{(R)}$. After clinical diagnosis was made, visual analogue scale(VAS) was used for evaluation of clinical progress of the subject's chief complaints. The author calculated VAS treatment index(VAS Ti) from the record of VAS. The more VAS Ti was, the less remission of subjective symptom was, The data were analyzed with SAS/Stat program and the results of this study were as follows: 1. There were no significant difference in all the variables of joint vibration by age and sex. 2. Integral and peak amplitude in patients of Angle's class I were higher than those of class II or III patients. Integral in patients of group function was higher than that in patients of canine guidance or other types of lateral excursion. 3. As to Angle's classification or lateral guidance type, there were almost not significant difference between subgroup of same class or type and subgroup of different class or type on both sides. And there were also almost not difference between one side and the other side related to preferred chewing side or affected side. 4. Patients with disk displacement with reduction showed higher value of integral and peak amplitude than any other patients. 5. Joint vibration variables significantly correlated with VAS Ti of pain. with clinical range of mouth opening, and with ingredients of mandibular torque rotational movement.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.4 no.2
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• pp.38-45
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• 1995

The diagnosis and monitoring system of abnormal cutting condition is necessary to realize precision machining proces and factory automation, which are final goal of metal cutting in order to develop this system, theimage processing technique has been investigated in machining process. In theis paper, the measurement system of tool wear using computer vision is designed to detect the wear pattern by non-contact and direct method and get the realiable wear information about cutting tool. We measured the area of the side and front part of the diamond core dril which is used in 40kHz ultrasonic vibration machine.