• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.6
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• pp.22-28
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• 2003

We should maintain the maximum operation capacity for production facilities and find properly out the fault of each equipment rapidly in order to decrease a loss caused by its failure. The acoustic signals of a machine always carry the dynamic information of the machine. These signals are very useful for the feature extraction and fault diagnosis. We performed a fundamental study which develops a system of fault diagnosis for a pump. We obtained noises by a microphone, analysed and compared the signals converted to Sequency range for normal products, artificially deformed products. We tried to search a change of noise signals according to machine malfunctions and analyse the type of deformation or failure. The results showed that acoustic signals as well as vibration signals can be used as a simple method for a detection of machine malfunction or fault diagnosis.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.99-104
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• 2003

We should maintain the minimum operation capacity for production facilities and find properly out the fault of each equipment rapidly in order to decrease a loss caused by its failure. The acoustic signals of a machine always carry the dynamic information of the machine. These signals are very useful for the feature extraction and fault diagnosis. We performed a fundamental study which develops a system of fault diagnosis for a pump. We obtained noises by a microphone, analysed and compared the signals converted to frequency range for normal products, artificially deformed products. We tried to search a change of noise signals according to machine malfunctions and analyse the type of deformation or failure. The results showed that acoustic signals as well as vibration signals can be used as a simple method for a detection of machine malfunction or fault diagnosis.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.883-887
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• 2002

We should maintain the maximum operation capacity for production facilities and find properly out the fault of each equipment rapidly in order to decrease a loss caused by its failure. The acoustic and vibration signals of a machine always carry the dynamic information of the machine. These signals are very useful fur the feature extraction and fault diagnosis. We performed a fundamental study which develops a system of fault diagnosis for a pump. We experimented vibrations by acceleration sensors and noises by microphones, compared and analysed for normal products, artificially deformed products. We tried to search a change of the dynamic signals according to machine malfunctions and analyse the type of deformation or failure. The results showed that acoustic signals as well as vibration signals can be used as a simple method lot a detection of machine malfunction or fault diagnosis.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1766-1769
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• 2005

Air-operated valve(AOV) is one of principal valves that are using to control fluid flow in nuclear power plants. AOV is suffered from damage and malfunction by the abrasion, corrosion and vibration of valve parts under the long time operation. This mechanical trouble and malfunction of valve is critical for the safety of power plant. So a periodic diagnosis for safety of power plants is inevitable to guarantee the safety of the power plant. But depending on the type of the actuator and valve body, various types of AOV exist. In this study, It is developed the diagnostic system that users of power plants are easy to handle in this paper.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.5
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• pp.1411-1418
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• 1996

Large scale plants are equipped with a number of the rotating machineries which ocuupy important positions in the plant system. Therefore, the most important one is a vibraiton diagnostic thchnology which can detect quickly any abnormal symptom of operating malfunction and guve operational and inspection guides adequately. A new diagnosis method is developed in this paper, in which the fuzzy set theory is introduced to diagnose the defects of ratating machinery. The selection of memgership function and the fuzzy operation model are discussed in datail here. The systme is sucessfully used for various defacts diagnosis of rotating machinery. The result indicate that realixtic application can be builtusing this approach.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.132-134
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• 2006

To improve equipment throughput and device yield, a malfunction in plasma equipment should be accurately diagnosed. A recognition model for plasma diagnosis was constructed by applying neural network to scanning electron microscope (SEM) image of plasma-etched patterns. The experimental data were collected from a plasma etching of tungsten thin films. Faults in plasma were generated by simulating a variation in process parameters. Feature vectors were obtained by applying direct and wavelet techniques to SEM Images. The wavelet techniques generated three feature vectors composed of detailed components. The diagnosis models constructed were evaluated in terms of the recognition accuracy. The direct technique yielded much smaller recognition accuracy with respect to the wavelet technique. The improvement was about 82%. This demonstrates that the direct method is more effective in constructing a neural network model of SEM profile information.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1067-1072
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• 2001

In this study, the vibration diagnosis program for onboard machinery has been developed. The developed program includes signal monitoring module, system diagnosis module, and system modification module. The signal monitoring module is to monitor the vibration signal in time and frequency domains. And the system diagnosis module, which is developed by using Neural Network with error back propagation algorithm, can detect the abnormal symptom indicating the malfunction of the machinery onboard ships. The investigations of the developed system are presented through the experiment using Rotor Kit. Abnormal vibration signals are created by adding additional weight, manually misaligning the shaft, and loosening the bolts.

• Journal of Korean Neurosurgical Society
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• v.60 no.1
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• pp.1-7
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• 2017

Objective : The diagnosis of shunt malfunction can be challenging since neuroimaging results are not always correlated with clinical outcomes. The purpose of this study was to evaluate the efficacy of a simple, minimally invasive cerebrospinal fluid (CSF) lumbar tapping test that predicts shunt under-drainage in hydrocephalus patients. Methods : We retrospectively reviewed the clinical and radiological features of 48 patients who underwent routine CSF lumbar tapping after ventriculoperitoneal shunt (VPS) operation using a programmable shunting device. We compared shunt valve opening pressure and CSF lumbar tapping pressure to check under-drainage. Results : The mean pressure difference between valve opening pressure and CSF lumbar tapping pressure of all patients were $2.21{\pm}24.57mmH_2O$. The frequency of CSF lumbar tapping was $2.06{\pm}1.26times$. Eighty five times lumbar tapping of 41 patients showed that their VPS function was normal which was consistent with clinical improvement and decreased ventricle size on computed tomography scan. The mean pressure difference in these patients was $-3.69{\pm}19.20mmH_2O$. The mean frequency of CSF lumbar tapping was $2.07{\pm}1.25times$. Fourteen cases of 10 patients revealed suspected VPS malfunction which were consistent with radiological results and clinical symptoms, defined as changes in ventricle size and no clinical improvement. The mean pressure difference was $38.07{\pm}23.58mmH_2O$. The mean frequency of CSF lumbar tapping was $1.44{\pm}1.01times$. Pressure difference greater than $35mmH_2O$ was shown in 2.35% of the normal VPS function group (2 of 85) whereas it was shown in 64.29% of the suspected VPS malfunction group (9 of 14). The difference was statistically significant (p=0.000001). Among 10 patients with under-drainage, 5 patients underwent shunt revision. The causes of the shunt malfunction included 3 cases of proximal occlusion and 2 cases of distal obstruction and valve malfunction. Conclusion : Under-drainage of CSF should be suspected if CSF lumbar tapping pressure is $35mmH_2O$ higher than the valve opening pressure and shunt malfunction evaluation or adjustment of the valve opening pressure should be made.

• Journal of the Korean institute of surface engineering
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• v.48 no.4
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• pp.179-184
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• 2015

Quadrupole Mass Spectrometers (QMS) is a very useful tool in vacuum process diagnosis. Tungsten filament based ion sources are vulnerable to contamination from process gas monitoring. Common symptoms of quadrupole mass spectrometer malfunction is appearance of unwanted contaminant mass peaks or no detection of any ion peaks. We disassembled used quadrupole mass spectrometer and found out black insulating deposits on inside of ion source parts. Five steps of cleaning procedure were applied and almost full restoration of functions were confirmed in two types of closed ion source quadrupole mass spectrometer. By using a numerical modeling (CFD-ACE+) technique, the electric potential profile of ion source with/without insulating deposit was calculated and showed the possibility of quadrupole mass spectrometer malfunction by the deterioration of designed potential profile inside the ion source.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.89-99
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• 2018

Downtime and malfunction of industrial rotor machines represents a crucial cost burden and productivity loss. Fault diagnosis of this equipment has recently been carried out to detect their fault(s) and cause(s) by using fault classification methods. However, these methods are of limited use in detecting rotor faults because of their hypersensitivity to unexpected and different equipment conditions individually. These limitations tend to affect the accuracy of fault classification since fault-related features calculated from vibration signal are moved to other regions or changed. To improve the limited diagnosis accuracy of existing methods, we propose a new approach for fault diagnosis of rotor machines based on the model generated by supervised learning. Our work is based on feature residual values from vibration signals as fault indices. Our diagnostic model is a robust and flexible process that, once learned from historical data only one time, allows it to apply to different target systems without optimization of algorithms. The performance of the proposed method was evaluated by comparing its results with conventional methods for fault diagnosis of rotor machines. The experimental results show that the proposed method can be used to achieve better fault diagnosis, even when applied to systems with different normal-state signals, scales, and structures, without tuning or the use of a complementary algorithm. The effectiveness of the method was assessed by simulation using various rotor machine models.