• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.4
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• pp.228-233
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• 2012

In power system, substation facilities have become too complex and larger according to an extended power system. Also, customers require the high quality of electrical power system. However, some facilities become old and often break down unexpectedly. The unexpected failure may cause a break in power system and loss of profits. Therefore it is important to prevent abrupt faults by monitoring the condition of power systems. Among the various power facilities, power transformers play an important role in the transmission and distribution systems. In this research, we develop intelligent diagnosis technique for monitoring of power transformer by FCM(Fuzzy c-means) and ELM(Extreme Learning Machine). The proposed technique make it possible to diagnosis the faults occurred in transformer. To demonstrate the validity of proposed method, various experiments are performed and their results are presented.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.969-970
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• 2007

In this thesis, an algorithm of fault detection and diagnosis during operation for induction motors under the condition of various loads and rates is investigated. For this purpose, the spectrum pattern of input cutterrents was used to monitor the state of induction motors, and by clustering the spectrum pattern of input currents, the newly occurrence of spectrums pattern caused by faults were detected. For diagnosis of the fault detected, the fuzzy fault tree was designed, and the fuzzy relation equation representing the relation between an induction motor fault and each fault type, was solved. The solution of the fuzzy relation equation shows the possibility of each fault's occurring.

• International Journal of Precision Engineering and Manufacturing
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• v.4 no.6
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• pp.44-49
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• 2003

The morphological analysis of wear debris can provide early a failure diagnosis in lubricated moving system. It can be effective to analyze operating conditions of oil-lubricated tribological system with shape characteristics of wear debris in a lubricant. But, in order to predict and recognize an operating condition of lubricated machine, it is needed to analyze and to identify shape characteristics of wear debris. Therefore, If the morphological characteristics of wear debris are recognized by computer image analysis using the neural network algorithm, it is possible to recognize operating condition of hydraulic driving members. In this study, wear debris in the lubricating oil are extracted by membrane filter (0.45$\mu\textrm{m}$), and the quantitative values of shape parameters of wear debris are calculated by the digital image processing. This shape parameters are studied and identified by the artificial neural network algorithm. The result of study could be applied to prediction and to recognition of the operating condition of hydraulic driving members in lubricated machine systems.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.852-856
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• 2004

An induction motor is one of the most popular electrical apparatuses owing to its simple structure and robust construction. Parameter identification of the induction motor has long been researched either for a vector control technique or fault detection. Since vector control is a well-established technique for induction motor control, this paper concentrates on successive identification of physical parameters with on-load data for the purpose of condition monitoring and/or fault detection. For extracting six physical parameters from the on-load data in the framework of the induction motor state equation, unmeasured initial state values and profiles of load torque have to be estimated as well. However, the analytic optimization methods in general fail to estimate these auxiliary but significant parameters owing to the difficulty of obtaining their gradient information. In this paper, the univariate dynamic encoding algorithm for searches (uDEAS) newly developed is applied to the identification of whole unknown parameters in the mathematical equations of an induction motor with normal operating data. Profiles of identified parameters appear to be reasonable and therefore the proposed approach is available for fault diagnosis of induction motors by monitoring physical parameters.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.11
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• pp.1037-1043
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• 2004

In this paper, a new fault detection and isolation algorithm fur inertial sensor system is proposed. To identify the inertial sensor fault, single antenna GPS receiver is used as an effective redundancy source. To use GPS receiver as redundancy for the inertial sensors, the algorithm to estimate the attitude and acceleration using single antenna GPS receiver is adopted. By using Doppler shift of carrier phase signal and kinetic characteristics of aircraft, attitude information of aircraft can be obtained at the coordinated flight condition. Based on this idea, fault diagnosis algorithm for inertial sensors using single antenna GPS based attitude is proposed. For more effective FDI, decision variables considering the aircraft maneuver are proposed. The effectiveness of the proposed algorithm is verified through the numerical simulations.

• Journal of Sasang Constitutional Medicine
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• v.24 no.4
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• pp.28-39
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• 2012

Objectives : This study is aimed to develop the algorithm to diagnose Taeeumin's symptomology, by the method of literature research on Sasang Constitutional Medicine. Methods : Applying the sequential differentiations of Taeeumin's symptomology, or exterior-interior disease differentiation, favorable-unfavorable pattern differentiation, and mild-severe-dangerous-urgent pattern differentiation, "Donguisusebowon" and related literatures have been reviewed. Results and Conclusions : 1) 1st step: Taeeumin's exterior pattern and interior pattern are differentiated by the indexes of whole-body cold or heat pattern, sweating, and facial complexion. 2) 2nd step: The favorable pattern of the Taeeumin's exterior disease can be detected by indexes of the existence of fever, generalized pain while the unfavorable one by indexes of the abnormal condition of digestion and feces, and fearful throbbing. The favorable pattern of the Taeeumin's interior disease can be diagnosed based on indexes of eye pain, dry nose, dry throat, and heat symptoms that occur in various parts of the body, while the unfavorable one by indexes of thirsty, urination, feces and specific symptoms which can be induced by dryness. And in the both unfavorable patterns the dark complexion on the faces is revealed. 3) 3rd step: The mild-severe patterns of the favorably exterior disease are differentiated in terms of the condition of fever, while the mild-severe patterns of the favorably interior disease are in differentiated based on whether abnormal symptoms are revealed in the gastrointestinal tract. Both of the unfavorably dangerous-urgent patterns in exterior and interior diseases are differentiated by the symptoms such as tinnitus, dim vision, weakness of legs and back pain, and lack of strength in legs and thighs.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.2
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• pp.147-152
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• 2016

This paper proposes a new TPC (Transmission Priority Change) algorithm which is used to diagnose failures of a CAN (Controller Area Network) based network system for the oil tank monitoring. The TPC algorithm is aimed to increase the total amount of data transmission and to minimize the latency for an urgent message by changing transmission priority. The urgency of the data transmission has been determined by the conditions of sensors. There are multiple sensors inside of the oil tank, such as temperature, valve, pressure and level sensors. When the sensors operate normally, the sensory data can be collected through the CAN network by the monitoring system. However when there is a dangerous situation or failure situation happened at a sensor, the data need to be handled quickly by the monitoring system, which is implemented by using the TPC algorithm. The effectiveness of the TPC algorithm has been verified by the real experiments. In addition, this paper introduces a method that people can figure out the condition of oil tanks and also can perform the fault diagnosis in real-time by using transmitted packet data. By applying this TPC algorithm to various industries, the convenience and reliability of multiple sensors network system can be improved.

• Journal of Sasang Constitutional Medicine
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• v.24 no.3
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• pp.34-41
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• 2012

Objectives In this study, we developed a mock-up of the system for Sasang Constitutional (SC) diagnosis. This system consists of 5 devices which are the face analyzer, the voice analyzer, the skin analyzer, the pulse analyzer, and the computer-based questionnaire. Our goal is to evaluate the repeatability of the system. Methods Each device is capable of classifying SC types. The classification probability of the integrated system for the SC types was obtained by summing the probability from each device. For evaluating the repeatability of the system, we collected data for 5 subjects, and repeated the measurement three times for each individual. The average and standard deviation were used for calculating the Coefficient of Variation. Results The results showed that the repeatability of the classification probability of the integrated system is about 8%, which implies the system is repeatable. Conclusions To increase usability of this system, it is desirable for the system to offer information on health condition of the user. The integrated constitutional diagnosis system will be upgraded to complement the convenience and to develop the diagnostic algorithm for the user's health condition.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.10
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• pp.799-807
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• 2002

The vibration signal can give an indication of the condition of rotating machinery, highlighting potential faults such as unbalance, misalignment and bearing defects. The features in the vibration signal provide an important source of information for the faults diagnosis of rotating machinery. When additional training data become available after the initial training is completed, the conventional neural networks (NNs) must be retrained by applying total data including additional training data. This paper proposes the fault diagnostics algorithm using the ART-Kohonen network which does not destroy the initial training and can adapt additional training data that is suitable for the classification of machine condition. The results of the experiments confirm that the proposed algorithm performs better than other NNs as the self-organizing feature maps (SOFM) , learning vector quantization (LYQ) and radial basis function (RBF) NNs with respect to classification quality. The classification success rate for the ART-Kohonen network was 94 o/o and for the SOFM, LYQ and RBF network were 93 %, 93 % and 89 % respectively.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.89-96
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• 2018

When a fault occurs in a power system, the existing analytic models for the power system fault diagnosis could generate multiple solutions under the condition of one or more protective relays (PRs) and/or circuit breakers (CBs) malfunctioning, and/or an alarm or alarms of these PRs and/or CBs failing. Therefore, this paper presents an improved analytic model addressing the above problem. It takes into account the interaction between the uncertainty involved with PR operation and CB tripping and the uncertainty of the alarm reception, which makes the analytic model more reasonable. In addition, the existing analytic models apply the penalty function method to deal with constraints, which is influenced by the artificial setting of the penalty factor. In order to avoid the penalty factor's effects, this paper transforms constraints into an objective function, and then puts forward an improved immune clonal multi-objective optimization algorithm to solve the optimal solution. Finally, the cases of the power system fault diagnosis are served for demonstrating the feasibility and efficiency of the proposed model and method.