• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.832-835
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• 2002

This paper presents a new approach integrated Case-Based Reasoning with Self. Organization Map(SOM) in diagnosis systems. The causes of faults are obtained by case-base trained from SOM. When the vibration problem of rotating machinery occurs, this provides an exact diagnosis method that shows the fault cause of vibration problem. In order to verify the performance of algorithm, we applied it to diagnose the fault cause of the electric motor.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.809-816
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• 2008

With increasing demands for reducing cost of maintenance which can detect machine fault automatically; low cost and intelligent functionality sensors are required. Rapid developments, in semiconductor, computing, and communication have led to a new generation of sensor called "smart" sensors with functionality and intelligence. The purpose of this research is comparison of machine fault classification between general analyzer signals and smart sensor signals. Three types of sensors are used in induction motors faults diagnosis, which are vibration, current and flux. Classification results are satisfied.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1090-1095
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• 2012

In this paper, we propose a model-based fault diagnosis method applied to vibration data. The fault detection is performed by comparing estimated parameters with normal parameters and deciding if the observed changes can be explained satisfactorily in terms of noise or undermodelling. The key feature of this method is that it accounts for the effects of noise and model mismatch. And we aslo design a classifier for the fault isolation by applying the multiclass SVM (Support Vector Machine) to the estimated parameters. The proposed fault detection and isolation methods are applied to an engine vibration data to show a good performance. The proposed fault detection method is compared with a signal-based fault detection method through a performance analysis.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.549-555
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• 2003

The technology of machine condition monitoring is used effectively to detect the machine faults at an early stage using different machine quantities, such as current, voltage, temperature and vibration. Induction motors are most widely used to drive pumps, compressors and fans in industrial drives. This paper presents approach to data fusion using Dempster-Shafer theory because only one technique has uncertainty. So we can obtain advanced accuracy of the machine fault diagnosis. Vibration and current quantities are applied to diagnose three-phase induction motor.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.330-331
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• 2014

A mathematical model is suggested for diagnosis on a reciprocating pump. To the end, kinematic, thermodynamic and fluidic analyses are carried out for a simplified reciprocating pump model. The pressure inside the cylinder is expressed as a function of the rotation angle of a crank axle. The mathematical model consists of one cylinder with suction and discharge valves and an accumulator. The effect of valve leakage on the discharge angle is investigated. The discharge angle difference between normal state and leakage state increases with the leakage extent.

• Journal of Power System Engineering
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• v.11 no.1
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• pp.45-50
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• 2007

Acoustic emission technology is applied to diagnosis the internal leak and operating conditions of the major valves at nuclear power plants. The purpose of this study is to verify availability of the acoustic emission as in-situ diagnosis method. In this study, acoustic emission tests are performed when the pressurized high temperature steam flowed through gate valve(1st stage reheater valve) and glove valve(main steam dump valve) on the normal size of 4 and 8". The valve internal leak diagnosis system for practical field was designed. The acoustic emission method was applied to the valves at the site, and the background noise was measured for the abnormal plant condition. To improve the reliability, a judgment of leak on the system was used various factors which are AE parameters, trend analysis, signal level analysis and RMS(root mean square) analysis of acoustic signal emitted from the valve operating condition internal leak.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.134-139
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• 2004

The correlation dimension of a nonlinear method for the diagnosis on the clearance of rotating machinery is introduced in this paper. The correlation dimension can provide some intrinsic information of an underlying dynamic system by reconstructing measured scalar time series. Vibration signals measured from a rotor with different operating conditions are analyzed using the correlation dimension. The results show that the correlation dimension method can identify the magnitude of the clearance of a rotor and the lubricating condition.

• Journal of KSNVE
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• v.5 no.3
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• pp.361-371
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• 1995

The vibration of reactor internals should be monitored and diagnosed for the early detection of the failure of reactor pressure vessel. This can be performed by analyzing the time-history signals from the excore neutron flux detertors. The conventional method is an on-demand system which generates power spectra through Fast Fourier Transform(FFT) algorithm. The operator can make his own decision to detect abnormal vibration using these spectra. This post- processing method, however, requires special expertise in the reactor noise analysis and signal processing for random data. It may mislead the operator into erroneous decision-making, if he is a novice in reactor noise analysis. Hence this study is focused on the automated monitoring and diagnosis procedure for the reactor noise analysis, especially on the Fuzzy algorithm to recognize the pattern of the vibration of Core Suport Barrel. The excore neutron signals of Yonggwang Nuclear Power Plant unit 3 is acquired and analyzed using conventional FFT spectra and tested to adopt the Fuzzy method. An Automated Monitoring and Diagnosis System for CSB Vibration using this Fuzzy method is proposed. Furthermore, vibration data for CSB of Youggwang Nnclear Power Plant unit 3 is presented.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10a
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• pp.836-841
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• 1987

Many kinds of conditioning monitoring technique have been studied, so this study has investigated the possibility of checking the trend in the fault diagnosis of ball bearing, one of the elements of rotating machine, by applying the cepstral analysis method using the adaptive noise cancelling (ANC) method. And computer simulation is conducted in oder to identify obviously the physical meaning of ANC. The optimal adaptation gain in adaptive filter is estimated, the performance of ANC according to the change of the signal to noise ratio and convergence of LMS algorithm is considered by simulation. It is verified that cepstral analysis using ANC method is more effective than the conventional cepstral analysis method in bearing fault diagnosis.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.774-779
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• 2001

If rotating machinery have a fault, we can detect it using vibration or noise signals. However some maintenance engineers who doesn't have expert knowledge, needs the help of vibration experts for diagnosing rotating machinery. But qualified experts are rare, therefore we have been developed the case based reasoning (CBR) system which is able to manipulate the past experiences of vibration diagnosis experts. In the CBR system, the maintenance engineers can retrieve too information from previous cases which are most similar to new problem and they can solve new problem using solutions from the previous cases. In this paper, we propose a new method which is the case retrieval of CBR system using Petri net and we also applied it to diagnosis for electric motors as a practical problem.