• Journal of KSNVE
• /
• v.10 no.2
• /
• pp.331-337
• /
• 2000

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal defect diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised learning algorithm is used to improve the quality of the classifier decision regions.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2014.10a
• /
• pp.472-475
• /
• 2014

Abnormal cab vibrations in the Y direction in commercial vehicles during driving(70~90kph) are not common vibrations that happen to vehicles during driving and can be an obstacle to normal driving. This study conducted Operation Deflection Shape(ODS) testing to identify the causes of those abnormal cab vibrations and find solutions for them and also a sine sweep test to find resonance and frequency in the cab suspension system and set directions for improvement. The study also altered the shape of the bush inner part for changes to the rigidity features of the cab bush in the Y direction and revised the design with optimal rigidity in the Y direction, thus improving abnormal cab vibrations in the Y direction during driving.

• Journal of Power System Engineering
• /
• v.5 no.3
• /
• pp.64-72
• /
• 2001

This paper describes an optimal design technology in a segment type vertical guide bearing for vertical rotating machinery. Segment type vertical guide bearings have widely used for vertical rotating machinery, however bearing problems, such as excessive vibration and temperature rise, frequently take place in the actual machine. Such excessive vibration magnitude and/or abnormal bearing metal temperature rise result in serious damage and economic losses. Thus the segment type vertical guide bearing should be designed to get optimal characteristics in order to maintain stable operation without bearing failure due to abnormal vibration and/or abnormal bearing metal temperature. The preload ratio is the most important parameter in designing the segment type vertical guide bearing. Because adjustment of the bearing preload by changing the bearing clearance could easily control both the bearing stiffness and the cooling effect. In the paper, the influence of the preload effects on the bearing metal temperature and the bearing stiffness has been investigated both theoretically and experimentally in order to find out an optimum preload ratio. Results show that the segment type vertical guide bearing has an optimum preload ratio at which the bearing stiffness reaches a masimum value while the bearing metal temperature is minimized.

• KEPCO Journal on Electric Power and Energy
• /
• v.4 no.2
• /
• pp.101-105
• /
• 2018

In this paper, the kernel regression model is applied for the case study of gas turbine abnormal state analysis. In addition to vibration analysis at the remote site, the kernel regression model technique can is useful for analyzing abnormal state of rotor vibration signals of gas turbine in power plant. In monitoring based on data-driven techniques correlated measurements, the fault free training data of shaft vibration obtained during normal operations of gas turbine are used to develop a empirical model based on auto-associative kernel regression. This data-driven model can be used to predict virtual measurements, which are compared with real-time data, generating residuals. Any faults in the system may cause statistically abnormal changes in these residuals and could be detected. As the result, the kernel regression model provides information that can distinguish anomalies such as sensor failure in a shaft vibration signal.

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

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2010.10a
• /
• pp.376-377
• /
• 2010

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.26 no.4
• /
• pp.469-474
• /
• 2016

Abnormal sound was heard from a heat exchanger of condensate water system in a nuclear power plant, which was identified as impact sound of a loose part later. Nuclear power plants are normally equipped with loose part monitoring system for primary water system, but not for secondary water system. The abnormal sound was analyzed by using the impact signal-processing methodology based on the Hertz theory. The predicted results for impact location and size of the loose part showed good agreement with those of the actual loose part found during the overhaul period in the plant. So, this analysis methodology for the impact signal will be widely utilized for the primary and secondary side of the nuclear power plant.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2006.05a
• /
• pp.1049-1052
• /
• 2006

Object of this study is to develop an Stack Silencer System for quieting abnormal noise around power plant. Abnormal noise gets resident people to feel uncomfortable. Stack Silencer System is to minimize pressure loss of exhaust gas and to maximize noise reduction effect from abnormal noise frequency band. Stack Silencer System is installation in stack and absorbing material is an aluminum foam. Reduction effect measures insertion loss of $8.2{\sim}19.4dB$ by Stack silencer installation. After Stack Silencer System installation, pressure loss of exhaust gas measured $5{\sim}9mmH_2O$.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2006.05a
• /
• pp.1290-1295
• /
• 2006

The 120 meters high pressure feedwater heater drain piping in nuclear power plant had been suffered by excessive vibration from the beginning of power generation. As time goes by, the piping vibration was beyond the allowable limit and an appropriate countermeasure was required to prevent the fatigue failure of the pipeline from the abnormal vibration. In this study, the vibrational characteristics of high pressure feedwater heater drain piping and the countermeasure for abnormal vibration were investigated. Among the several vibration reduction methods, the piping layout changed by making the smooth pipeline was applied to the high Pressure feedwater heater drain piping in nuclear Power plant. Applying the countermeasure, the vibration level was found to reduce over 54 percents and was satisfied under the allowable velocity at the full-power operation condition.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2009.10a
• /
• pp.607-610
• /
• 2009

The abnormal combustion in the running engine results to knocking which increases the pressure and temperature in the cylinder, thereby decreasing the generated power by reducing the thermal efficiency. When the temperature and pressure in the cylinder increased rapidly by knocking, abnormal combustion takes place and the engine power is decreased. To investigate the knocking phenomenon, accelerometers are installed in the cylinder head to monitor and diagnose the vibration signal. As method of signal analysis, the time-frequency analysis method was adapted for acquisition of vibration signal and analyzes engine combustion in the short time. In this experiment, after analyzing time data which is stored in the signal recorder in one unit work (4 strokes: 2 revolutions), the signal with frequency and Wavelet methods with extracted one engine combustion data was also analyzed. Then, normal condition with no knocking signal is analyzed at this time. Hereafter, the experiments made a standard for distinguishing normal and abnormal condition to be carried out in acquisition of vibration signal at all cylinders and extracting knocking signal. In addition, analyzing methods can be diverse with Symmetry Dot Patterns (SDP), Time Synchronous Average (TSA), Wigner-Ville Distribution (WVD), Wigner-Ville Spectrum (WVS) and Mean Instantaneous Power (MIP) in the cold test [2]. With signal processing of vibration from engine knocking sensor, the authors adapted a part of engine /rotor vibration analysis and monitoring system for marine vessels to prevent several problems due to engine knocking