• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.2041_2042
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• 2009

The diagnostic/prognostic problems for condition based maintenance or Prognostics and Health Management has been used. Primary objectives of diagnosis/prognosis are maximizing system availability and minimizing downtime from fault isolation through more effective troubleshooting efforts. Diagnosis aims to detect the onset of failures to improve system performance and reduce life cycle cost by reducing the failure time. The prognosis can reduce operational and support total ownership cost and improve safety of machinery and complex systems. In this Paper, a fault diagnosis methodology has been described using a monopropellant propulsion system model as a test bench.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.12
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• pp.1233-1240
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• 2004

Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the nitration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.537-543
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• 2003

Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the vibration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.60 no.3
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• pp.114-119
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• 2011

Large Power transformer is a complex and critical component of power plant and consists of cellulosic paper, insulation oil, core, coil etc. Insulation materials of transformer and related equipment break down to liberate dissolved gas due to corona, partial discharge, pyrolysis or thermal decomposition. The dissolved gas kinds can be related to the type of electrical faults, and the rate of gas generation can indicate the severity of the fault. The identities of gases being generated are using very useful to decide the condition of transformation status. Therefore dissolved gas analysis is one of the best condition monitoring methods for power transformer. Also, on-line multi-gas analyzer has been developed and installed to monitor the condition of critical transformers. Rogers method, IEC method, key gas method and Duval Triangle method are used to failure diagnosis typically, and those methods are using the ratio or kinds of dissolved gas to evaluate the condition of transformer. This paper analyzes the reliability of transformer diagnostic methods considering actual dissolved gas concentration. Fault diagnosis is performed based on the dissolved gas of five transformers which experienced various fault respectively in the field, and the diagnosis result is compared with the actual off-line fault analysis. In this comparison result, Diagnostic methods using dissolved gas ratio like Rogers method, IEC method are sometimes fall outside the ratio code and no diagnosis but Duval triangle method and Key gas method is correct comparatively.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.425-430
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• 2008

To verify the system performance of portable AE leak diagnosis system which can measure with moving conditions, AE activities such as RMS voltage level, AE signal trend, leak rate degree according to AE database, FFT spectrum were measured during operation on total 11 valves of the secondary system in nuclear power plant. AE activities were recorded and analyzed from various operating conditions including different temperature, type of valve, pressure difference, valve size and fluid. The results of this field study are utilized to select the type of sensors, the frequency band for filtering and thereby to improve the signal-to-noise ratio for diagnosis for diagnosis or monitoring of valves in operation. As the final result of application study above, portable type leak diagnosis system by AE was developed. The outcome of the study can be definitely applied as a means of the diagnosis or monitoring system for energy saving and prevention of accident for power plant valve. The purpose of this study is to verify availability of the acoustic emission in-situ monitoring method to the internal leak and operating conditions of the major valves at nuclear power plants. In this study, acoustic emission tests are performed when the pressurized temperature water and steam flowed through glove valve(main steam dump valve) and check valve(main steam outlet pump check valve) on the normal size of 12 and 18 ". The valve internal leak monitoring 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, frequency analysis, voltage analysis and amplitude analysis of acoustic signal emitted from the valve operating condition internal leak.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.11
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• pp.2051-2057
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• 2007

In Oriental medicine, the status of a tongue is the important indicator to diagnose one's health like physiological and clinicopathological changes of inner parts of the body. The method of tongue diagnosis is not only convenient but also non-invasive and widely used in Oriental medicine. However, tongue diagnosis is affected by examination circumstances a lot like a light source, patient's posture and doctor's condition. To develop an automatic tongue diagnosis system for an objective and standardized diagnosis, segmenting a tongue is inevitable but difficult since the colors of a tongue, lips and skin in a mouth are similar. The proposed method includes preprocessing, graph-based over-segmentation, detecting positions with a local minimum over shading, detecting edge with color difference and estimating edge geometry from the probable structure of a tongue, where preprocessing performs down-sampling to reduce computation time, histogram equalization and edge enhancement. A tongue was segmented from a face image with a tongue from a digital tongue diagnosis system by the proposed method. According to three oriental medical doctors' evaluation, it produced the segmented region to include effective information and exclude a non-tongue region. It can be used to make an objective and standardized diagnosis.

• Journal of Power Electronics
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• v.13 no.6
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• pp.985-990
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• 2013

This paper proposes a novel fault diagnosis scheme using parameter estimation of the stator resistance, especially in the case of the open-phase faults of PMSM drives. The stator resistance of PMSMs can be estimated by the recursive least square (RLS) algorithm in real time. Fault diagnosis is achieved by analyzing the estimated stator resistance of each phase according to the fault condition. The proposed fault diagnosis scheme is implemented without any extra devices. Moreover, the estimated parameter information can be used to improve the control performance. The feasibility of the proposed fault diagnosis scheme is verified by simulation and experimental results.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.401-406
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine(SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel(KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.46-53
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine (SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel (KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.78-82
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• 2001

Electronic Weighting Machine is used an electronic scale which has many trouble because of broken load cells. In this paper, we propose an intelligent Diagnosis System will for an electronic weighting machine using fuzzy logic. It's purpose be detect of the load cell's trouble. The electronic circuit of system, which call 'junction box', will be connected resistances in a series at circuit of Wheatstone Bridge for monitoring the condition of load cells.