• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.952-954
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• 1998

This paper presents an artificial neural network approach to diagnose and detect faults in oil-filled power transformers based on dissolved gas analysis. The proposed algorithm is used to detect faults with or without cellulose involved. Several neural network topologies have been considered. Good diagnosis accuracy is obtained with the proposed approach.

• International Journal of Air-Conditioning and Refrigeration
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• v.9 no.3
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• pp.63-71
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• 2001

This paper presents a model-based method for detecting and diagnosing some faults in the cooling tower of healing, ventilating, and air-conditioning systems. A simple model for the cooling tower is employed. Faults in cooling tower operation are detected through the deviations in the values of system characteristic parameters such as the heat transfer coefficient-area product, the tower approach, the tower effectiveness, and fan power. Three distinct faults are considered: cooling tower inlet water temperature sensor fault, cooling tower pump fault, and cooling tower fan fault. As a result, most values of the system characteristics parameter variations due to a fault are much higher or lower than the values without faults. This allows the faults in a cooling tower to be detected easily using above methods. The diagnostic rules for the faults were also developed through investigating the changes in the different parameter due to each faults.

• Journal of KSNVE
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• v.10 no.6
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• pp.985-990
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• 2000

The signals that can be obtained from rotating machines often convey the information of machine. For example, if the machine under investigation has faults, then these signals often have pulse signals, embedded in noise. Therefore the ability to detect the fault signal in noise is major concern of fault diagnosis of rotating machine, In this paper, minimum variance cepstrum (MV cepstrum) . which can easily detect impulse in noise, has been applied to detect the type of faults of ball bearing system. To test the performance of this technique. various experiments have been performed for ball bearing elements that have man made faults. Results show that minimum variance cepstrum can easily detect the periodicity due to faults and also shows the pattern of excitation by the faults.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.814-826
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• 2023

Sensor faults in nuclear power plant instrumentation have the potential to spread negative effects from wrong signals that can cause an accident misdiagnosis by plant operators. To detect sensor faults and make accurate accident diagnoses, prior studies have developed a supervised learning-based sensor fault detection model and an accident diagnosis model with faulty sensor isolation. Even though the developed neural network models demonstrated satisfactory performance, their diagnosis performance should be reevaluated considering real-time connection. When operating in real-time, the diagnosis model is expected to indiscriminately accept fault data before receiving delayed fault information transferred from the previous fault detection model. The uncertainty of neural networks can also have a significant impact following the sensor fault features. In the present work, a pilot study was conducted to connect two models and observe actual outcomes from a real-time application with an integrated system. While the initial results showed an overall successful diagnosis, some issues were observed. To recover the diagnosis performance degradations, additive logics were applied to minimize the diagnosis failures that were not observed in the previous validations of the separate models. The results of a case study were then analyzed in terms of the real-time diagnosis outputs that plant operators would actually face in an emergency situation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.411-416
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• 2006

Fault detection and diagnosis is the most important technology in condition-based maintenance(CBM) system that usually begins from collecting signatures of running machines using multiple sensors for subsequent accurate analysis. With the quick development in industry, there is an increasing requirement of selecting special sensors that are cheap, robust, and easy-installation. This paper experimentally investigated performances of four types of sensors used in induction motors faults diagnosis, which are vibration, current, voltage and flux. In addition, diagnostic effects of five popular classifiers also were evaluated. First, the raw signals from the four types of sensors are collected at the same time. Then the features are calculated from collected signals. Next, these features are classified through five classifiers using artificial intelligence techniques. Finally, conclusions are given based on the experiment results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.4
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• pp.643-649
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• 2012

The diagnosis technique of stator winding faults based on Motor Current Signature Analysis(MCSA) was suggested. Park's vector pattern, the circle that is drawn by d-q transformed currents($i_d$, $i_q$), is widely used for stator winding faults detection. The current Distortion Ratio(DR), defined by the ratio of max axis and min axis of ellipse of Park's vector's pattern, was more simple and powerful method than the Park's vector pattern. In this study, a calculation method of distortion ratio of Park's vector pattern was suggested for auto diagnosis of stator winding short fault and usefulness of suggested calculation method of distortion ratio was verified through simulation using LabVIEW program.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.125-127
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• 2000

Multiple faults are hard to diagnose correctly because the operation of circuit breakers tripped by former fault changes the topology of power systems. The information including operating time of actuated relays and tripped circuit breakers is used for considering changes of the network topology in fault section diagnosis. This paper presents a method for fault section diagnosis using a set of matrices which represent changes of the network topology due to operation of circuit breakers. The proposed method uses fuzzy relation to cope with the unavoidable uncertainties imposed on fault section diagnosis of power systems. The inference executed by the proposed matrices provides the fault section candidates in the form of a matrix made up of the degree of membership. Experimental studies for real power systems reveal usefulness of the proposed technique to diagnose multiple faults.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.92-97
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• 2001

The major faults of CNC machine tool is operational error which is charge over 70%. This paper describes model of remote service and fault diagnosis for CNC machine tool with open architecture controller. For intelligent fault diagnosis, new model is proposed. In this paper, the three major operational faults, emergency stop error, cycle start disable and machine ready disable, are defined. Two diagnostic models based on the ladder diagram, switching function model, step switching function model, are proposed. For internet based remote service, suitable environment is proposed and implemented with web server and client.

• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.452-453
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• 2010

This paper proposes a novel diagnosis scheme using Extend Kalman Filter (EKF), especially, in subject to the open-phase faults of the inverter switches. The stator resistances of PMSM are estimated by the EKF in real time. The proposed diagnosis scheme is implemented without any extra devices. Moreover, since it uses a simple algorithm by analyzing only estimated stator resistances of each phase, the detection speed becomes fast. The feasibility of the proposed fault diagnosis scheme is proved by several simulation and experimental results.

• Journal of Power System Engineering
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• v.9 no.4
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• pp.187-193
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• 2005

In this paper, we proposes a man-machine interface design for fault diagnosis system with inter-node search method in a Petri net model. First, complicated fault cases are modeled as the Petri net graph expressions. Next, to find out causes of the faults on which we focus, a Petri net model is analyzed using the backward reasoning of transition-invariance in the Petri net. In this step, the inter-node search method algorithm is applied to the Petri net model for reducing the range of sources in faults. Finally, the proposed method is applied to a fault diagnosis of a refrigeration system to confirm the validity of the proposed method.