• KEPCO Journal on Electric Power and Energy
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• 제4권2호
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• pp.89-99
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• 2018

Downtime and malfunction of industrial rotor machines represents a crucial cost burden and productivity loss. Fault diagnosis of this equipment has recently been carried out to detect their fault(s) and cause(s) by using fault classification methods. However, these methods are of limited use in detecting rotor faults because of their hypersensitivity to unexpected and different equipment conditions individually. These limitations tend to affect the accuracy of fault classification since fault-related features calculated from vibration signal are moved to other regions or changed. To improve the limited diagnosis accuracy of existing methods, we propose a new approach for fault diagnosis of rotor machines based on the model generated by supervised learning. Our work is based on feature residual values from vibration signals as fault indices. Our diagnostic model is a robust and flexible process that, once learned from historical data only one time, allows it to apply to different target systems without optimization of algorithms. The performance of the proposed method was evaluated by comparing its results with conventional methods for fault diagnosis of rotor machines. The experimental results show that the proposed method can be used to achieve better fault diagnosis, even when applied to systems with different normal-state signals, scales, and structures, without tuning or the use of a complementary algorithm. The effectiveness of the method was assessed by simulation using various rotor machine models.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.2052-2053
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• 2008

High voltage motors are the core equipment of electric generation facilities and need to guarantee stable operation. Therefore, keeping healthy state is of importance by means of insulation diagnosis periodically. However, if the diagnosis went through in the different circumstances such as temperature, humidity, and impedance applied to the test equipment, the trend analysis have no significance. This paper presents the experimental results supporting the importance of keeping the same conditions under insulation diagnosis.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 전기설비
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• pp.31-33
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• 2005

The accelerating of industrial development causes electricity demand to increase. By that power equipments need high power, multi function and intelligence. Also consumers demand for guarantee power supplying of good quality and reasonable operating equipment. Also they require for reliance and stabilization of power facility. Therefore preventive maintenance of electric installation must be developed and improvement of domestic technical level is needed in the maintenance management of equipment. The diagnosis of trouble existence is technique that compares steady state with unusual condition, whereas the prediction technique makes a diagnosis of remaining equipments life. It is difficult for us to diagnose trouble existence of electric installation and to develop prediction method in building because of a wide scope for electric installation in building. And in this paper we will investigate diagnosis and prediction method for only switch part of electric installation in building.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2011년도 정기총회 및 추계학술대회 논문집
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• pp.2015-2020
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• 2011

Key set the current non-urban rail power failure of equipment to operate the railway will generate a lot of problems. I know in advance the ideal of power equipment to prevent the failure to derive an efficient replacement cycle for the power equipment monitoring and diagnosis of the condition can become necessary to develop a system. In order to determine the status of the power plant during operation and behavior of the partial discharge current state data, regular monitoring and inspection requirements also stamp the basic current, voltage, temperature and humidity should be identified.

• KEPCO Journal on Electric Power and Energy
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• 제2권2호
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• pp.293-298
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• 2016

Recently, interests for maintenance of transmission tower are increasing to extend life of structures and reduce maintenance cost. However, existing classical diagnosis method of corrosion deteriorated degree on the transmission tower steel members, visual inspection, has a problem that error often due to difference of inspector's individual knowledge and experience. In order to solve the problem, this study carried out to develop the corrosion deterioration inspection tool for transmission tower steel members. This tool is composed of camera equipment and computer-aided diagnosis system. We standardized the photographing method by camera equipment to obtain suitable pictures for image processing. Diagnosis system was designed to evaluate automatically degree of corrosion deterioration for member of transmission tower on the basis of the RGB color image processing techniques. It is anticipated that developed the corrosion deterioration inspection tool will be very helpful in decision of optimal maintenance time for transmission tower corrosion.

• 한국산학기술학회논문지
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• 제10권9호
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• pp.2313-2318
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• 2009

본 논문에서는 RFID 기반 하에 발전설비를 관리할 때, 상태정보를 효율적으로 관리하기 위한 방안으로 필요한 RFID 주파수 인식 성능을 분석하였다. 제안된 논문은 모니터링 서비스에서 발전설비의 고장 상태를 진단하기 위해서, 발전설비 및 주요 자재에 부착시, 인식 성능의 최적화를 목적으로 연구되었다. 본 논문의 결과는 발전설비에 RFID를 적용하는 환경에 유용할 것으로 사료된다.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2004년도 춘계학술대회 논문집
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• pp.502-505
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• 2004

The purpose of this experiment is to modify diagnosis criterion of isothermal relaxation current(IRC) measurement equipment which is using for distribution cable diagnosis. We're using this system for several years in the field instead of DC leakage current measurement and lots of cables were replaced. But we have to investigate on the reliability of this equipment for our cables because we didn't carried out condition assessment of extracted cables after field diagnosis by this equipment. It is important thing for cable maintenance. If the replacement criterion is improper we can not prevent failures or will waste budget on account of replacement of the sound cables. In this papar we selected field installed cables and injected silicone fluid to the cables for insulation rehabilitation. In order to prove reliability of the diagnosis equipment we compared diagnosis results and AC breakdown strength according to operating time after silicone treatment. This is the results of the field test for 1 year.

• Nuclear Engineering and Technology
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• 제56권8호
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• pp.2958-2973
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• 2024

Sliding bearings are crucial rotating mechanical components in nuclear power plants, and their failures can result in severe economic losses and human casualties. Deep learning provides a new approach to bearing fault diagnosis, but there is currently a lack of a universal fault diagnosis model for studying bearing-rotor systems under various operating conditions, speeds and faults. Research on bearing-rotor systems supported by sliding bearings is limited, leading to insufficient fault data. To address these issues, this paper proposes a fault diagnosis model framework for bearing-rotor systems based on a deep convolutional generative adversarial network (TF-DLGAN). This model not only exhibits outstanding fault diagnosis performance but also addresses the issue of insufficient fault data. An experimental platform is constructed to conduct fault experiments under various operating conditions, speeds and faults, establishing a dataset for sliding bearing-rotor system faults. Finally, the model's effectiveness is validated using this dataset.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2000년도 춘계학술대회 논문집 유기절연재료 방전 플라즈마
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• pp.18-22
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• 2000

The partial discharge testing is widely used in insulation property measurement because it gives low stress to high voltage equipment which is undertaken tests. Therefore it is very useful method compare to previous destructive methods and effective diagnosis method in power transformer that requires live-line diagnosis. But partial discharges have very complex characteristics of discharge pattern so it is required continuous research to development of precise analysis method. In recent, the study of partial discharge is carrying out discover of initial defect of power equipment through condition diagnosis and system development of degradation diagnosis using HFPD(High Frequency Partial Discharge) detection. In this study, simulated transformer is manufactured and HFPD occurred from transformer is measured with broad band antenna in real time, the degradation grade of transformer is analyzed through produced patterns in simulated transformer according to applied voltages.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2000년도 춘계학술대회 논문집 유기절연재료 방전 플라즈마
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• pp.29-32
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• 2000

The partial discharge testing is widely used in insulation property measurement because it gives low stress to high voltage equipment which is undertaken tests. Therefore it is very useful method compare to previous destructive methods and effective diagnosis method in power transformer that requires on-line & on-site diagnosis. But partial discharges have very complex characteristics of discharge pattern so it is required continuous research to development of precise analysis method. In recent, the study of partial discharge is carrying out discover of initial defect of power equipment through condition diagnosis and system development of degradation diagnosis using HFPD(High Frequency Partial Discharge) detection. In this study, simulated transformer is manufactured and PD/HFPD occurred from transformer is measured with broad band antenna in real time, the degradation grade of transformer is analyzed through produced patterns in simulated transformer according to applied voltages.