• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.1
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• pp.99-111
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• 2016

This paper presents an integrated fault diagnosis algorithm for driving motor of In-wheel independent drive electric vehicle. Especially, this paper proposes a method that integrated the high level fault diagnosis and the low level fault diagnosis in order to improve a robustness and performance of the fault diagnosis system. The high level fault diagnosis is performed using the vehicle dynamics analysis and the low level fault diagnosis is carried using the motor system analysis. The validity of the high level fault diagnosis algorithms was verified through $Carsim^{(R)}$ and MATLAB/$Simulink^{(R)}$ cosimulation and the low level fault diagnosis's validity was shown by applying it to a MATLAB/$Simulink^{(R)}$ interior permanent magnet synchronous motor control system. Finally, this paper presents a fault diagnosis strategy by combining the high level fault diagnosis and the low level fault diagnosis.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.9
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• pp.512-519
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• 2000

A Neural networks has been used for a expert system and fault diagnosis system. It is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping.쪼두 a fault occur in system a state of system is changed with transient state. Because of a previous state signal is considered as a information DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• Journal of Electrical Engineering and Technology
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• v.3 no.3
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• pp.293-299
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• 2008

One of the problems with the fault diagnosis of transformers based on dissolved gas is the inability to match the result of the different standards of fault diagnosis with real world standards. In this paper, the results of the different standards are analyzed using fuzzy logic and then compared with the empirical test. The proposed method is based on the standards and guidelines of the International Electrotechnical Commission (IEC), the Central Electric Generating Board (CEGB), and the American Society for Testing and Material (ASTM) and its main task is to assist the conventional gas ratio method. The comparison between the suggested method and existing methods indicates the capability of the suggested method in the on-line fault diagnosis of transformers. In addition, in some cases the existing standards are not able to diagnose the fault. For theses instances, the presented method has the potential of diagnosing the fault. In this paper, the information of three real transformers is used to show the capability of the suggested method in diagnosing the fault. The results validate the capability of the presented method in fault diagnosis of the transformer.

• Journal of information and communication convergence engineering
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• v.6 no.2
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• pp.207-212
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• 2008

There are several sensors in the liquid cargo ship. In the liquid cargo ship, we can get values from various sensors that are level sensor, temperature sensor, pressure sensor, oxygen sensor, VOCs sensor, high overfill sensor, etc. It is important to guarantee the reliability of sensors. In order to guarantee the reliability of sensors, we have to study the diagnosis of sensor fault. The technology of smart sensor is widely used. In this paper, the technology of smart sensor is applied to diagnosis of level sensor fault for liquid cargo ship. In order to diagnose sensor fault and find the sensor position, in this paper, we proposed algorithms of diagnosis of sensor fault using independent sensor diagnosis unit and self fault diagnosis using sensor modeling. Proposed methods are demonstrated by experiment and simulation. The results show that the proposed approach is useful. Proposed methods are useful to develop smart level sensor.

• JSTS:Journal of Semiconductor Technology and Science
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• v.7 no.4
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• pp.221-228
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• 2007

In this paper, we propose a new symbolic simulation for scan chain diagnosis to solve the diagnosis resolution problem. The proposed scan chain fault simulation, called the SF-simulation, is able to analyze the effects caused by faulty scan cells in good scan chains. A new scan chain fault simulation is performed with a modified logic ATPG pattern. In this simulation, we consider the effect of errors caused by scan shifting in the faulty scan chain. Therefore, for scan chain diagnosis, we use the faulty information in good scan chains which are not contaminated by the faults while unloading scan out responses. The SF-simulation can tighten the size of the candidate list and achieve a high diagnosis resolution by analyzing fault effects of good scan chains, which are ignored by most previous works. Experimental results demonstrate the effectiveness of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.4
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• pp.194-199
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• 2017

Power transformers are an important factor for power transmission and cause fatal losses if faults occur. Various diagnostic methods have been applied to predict the failure and to identify the cause of the failure. Typical diagnostic methods include the IEC diagnostic method, the Duval diagnostic method, the Rogers diagnostic method, and the Doernenburg diagnostic method using the ratio of the main gas. However, each diagnostic method has a disadvantage in that it can't diagnose the state of the power transformer unless the gas ratio is within the defined range. In order to solve these problems, we propose a diagnosis method using ELM based intelligent algorithm and fuzzy membership function. The final diagnosis is performed by multiplying the result of diagnosis in the four diagnostic methods (IEC, Duval, Rogers, and Doernenburg) by the fuzzy membership values. To show its effectiveness, the proposed fault diagnostic system has been intensively tested with the dissolved gases acquired from various power transformers.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.3
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• pp.268-274
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• 2010

Urban rail transit brings the benefits of various aspects of society. With the advent of fast and large trains, however, risk and scale of accidents have increased. Despite the fact that there is various safety features built into the modern metros, train faults happen from time to time. Especially, as urban railway vehicles in Korea have become deterioration rapidly, more advanced diagnosis methods are required to prevent various accidents. In this paper, we present diagnosis method for electrical wires to guarantee secure driving and authenticity more accurately in urban railway vehicles. Although there are kinds of conventional methods based on insulation resistance measurement and withstand test, it is extremely difficult to effectively diagnose obsolete equipments such as electrical wires and cables not new ones. This study is focused on development of diagnosis method and establishment of evaluation standard for electrical wires in urban railway vehicles.

• Fire Science and Engineering
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• v.21 no.2 s.66
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• pp.42-47
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• 2007

This paper presents the development of a case-based system for an electrical fire cause diagnosis system using the entity relation database. The relation database which provides a very simple but powerful way of representing data is widely used. The system focused on database construction and cause diagnosis can diagnose the causes of electrical fires easily and efficiently. In order to store and access to the information concerned with electrical fires, the key index items which identify electrical fires uniquely are derived out. The case-based system consists of a case which contains information from the past fires. The case-based system could present the cause of a newly occurred fire to be diagnosed by searching the case-based database for reasonable matching. The case-based system has not only searching functions with multiple attributes by using the collected various information(such as fire evidence, structure, and weather of a fire scene) but also more improved diagnosis functions which can be easily used for the electrical fire cause diagnosis system.

• Journal of IKEEE
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• v.27 no.4
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• pp.601-609
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• 2023

In this paper, we propose an artificial intelligence-based high-speed fault diagnosis method using an FPGA in the event of a ground fault in a DC system. When applying artificial intelligence algorithms to fault diagnosis, a substantial amount of computation and real-time data processing are required. By employing an FPGA with AI-based high-speed fault diagnosis, the DC breaker can operate more rapidly, thereby reducing the breaking capacity of the DC breaker. therefore, in this paper, an intelligent high-speed diagnosis algorithm was implemented by collecting fault data through fault simulation of a DC system using Matlab/Simulink. Subsequently, the proposed intelligent high-speed fault diagnosis algorithm was applied to the FPGA, and performance verification was conducted.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.154-156
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• 1993

In this paper, fault analysis using simulation method and fault diagnosis scheme are presented for induction motor drive system. Major faults such as inverter 'a' phase open fault, inverter 'a'-'b' phase short circuit fault and inverter 'a' phase ground fault are analyzed and simulated. On-line and off-line fault diagnosis systems are proposed.