• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.374-376
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• 2001

In this paper, we developed the fault diagnostic expert system of silicone-immersed transformer using dissolved gas analysis. The knowledge base module consists of the knowledge using the rule: if Then . The inference engine uses the fuzzy rule for the management of uncertainty of the boundary and rule and derivate the Belief and Plausibility of the normality and fault using Dempster-Shafer theory. The expert system is connected to the database and it can manages the history of gas-data of the transformer.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.90-93
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• 2014

In this paper, we have developed a new fault detection method based on vibration signal for rotor machinery. Generally, many methods related to detection of rotor fault exist and more advanced methods are continuously developing past several years. However, there are some problems with existing methods. Oftentimes, the accuracy of fault detection is affected by vibration signal change due to change of operating environment since the diagnostic model for rotor machinery is built by the data obtained from the system. To settle a this problems, we build a rotor diagnostic model by using feature residual based on vibration signal. To prove the algorithm's performance, a comparison between proposed method and the most used method on the rotor machinery was conducted. The experimental results demonstrate that the new approach can enhance and keeps the accuracy of fault detection exactly although the algorithm was applied to various systems.

• Journal of the Korean Society of Propulsion Engineers
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• v.15 no.2
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• pp.1-7
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• 2011

The UAV(Unmanned Aerial Vehicle) which is remotely operating with long endurance in high altitude must have a very reliable propulsion system. The precise fault diagnostic system of the turboprop engine as a propulsion system of this type UAV can promote reliability and availability. This work proposes a diagnostic method which can identify the faulted components from engine measuring parameter changes using Fuzzy Logic and quantify its faults from the identified fault pattern using Neural Network Algorithms. It is found by evaluation examples that the proposed diagnostic method can detect well not only single type faults but also multiple type faults.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2010.11a
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• pp.499-505
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• 2010

The UAV(Unmanned Aerial Vehicle) which is remotely operating with long endurance in high altitude must have a very reliable propulsion system. The precise fault diagnostic system of the turboprop engine as a propulsion system of this type UAV can promote reliability and availability. This work proposes a diagnostic method which can identify the faulted components from engine measuring parameter changes using Fuzzy Logic and quantify its faults from the identified fault pattern using Neural Network Algorithms. It is found by evaluation examples that the proposed diagnostic method can detect well not only single type faults but also multiple type faults.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.5
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• pp.110-123
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• 1994

The development of automatic production systems have required inteligent diagnostic and monitoring function to repair system failure and reduce production loss by the failure. In order to perform accurate functions of intelligent system, inferencing about total system failure and fault analysis due to each mechanical component failures are required. Also the solution about repair and maintenance can be suggested from these analysis results. As an essential component of mechanical system, a bearing system is investigated to define the failure behavior. The bearing failure is caused by lubricant system failure, metallurgical defficiency, mechanical condition(vibration, overloading, misalignment) and environmental effect. This study described roller bearing fault train due to stress variation and metallurgical defficiency from lubricant failure by using FTA.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.1
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• pp.19-25
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• 1998

Remote fault diagnosis and maintenance system using general telecommunication network is necessary for an effective fault diagnosis and higher productivity of NC machine tools. In order to monitor machine tool condition and diagnose alarm states due to electrical and mechanical faults, a remote data communication system for monitoring of NC machine fault diagnosis and status is developed. The developed system consists of (1) remote communication module among NC's and host PC using PSTN. (2) 8 channels analog data sensing module, (3) digital I/O module for control or NC machine, (4) communication module between NC machine and remote data communication system via RS-232C, and (5) software man-machine interface. Diagnostic monitoring results generated through a successive type inference engine are displayed in user-friendly graphics. The validity and reliability of the developed system is verified to be a powerful commercial version on a vertical machining center through a series of experiments.

• 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.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1189-1192
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• 1996

In this paper, a fault diagnosis method is proposed for self-diagnostics of PLC(Programmable Logic Controller), process controller in industrial fields, and diagnosis of its external devices such as sensors and actuators. The aim of this research is proposition of systematic method of fault diagnosis of PLC control system and development of its equipment. A PLC fault diagnosis algorithm consists of self-diagnostics given by PLC makers, Inpuot/Output tracking method by analyzing sequence PLC programs, searching method of past fault cases in database using an expert system, and diagnosis of PLC units such as CPU, DI, and DO board. Finally usability of PLC fault diagnostic system is verified by testing a MELSEC PLC.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.11 s.242
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• pp.1220-1228
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• 2005

Fault detection and diagnostic (FDD) systems have the potential to reduce equipment downtime, service costs, and utility costs. In this study, model based algorithm and fuzzy algorithm were used to detect and diagnose various fault at System multi-air conditioner. various fault include the Refrigerant Low charging, Fouling of Indoor Heat Exchanger, Fouling of Outdoor Heat Exchanger A experimental verification was conducted in the 6HP System multi-air conditioner on an 8-floor building. Test results showed diagnosis result about 78 $\~$ 90$\%$ for given faults. This Study lays the foundation fur future work on develope the real-time fault detection and diagnosis system for the System multi-air conditioner.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.592-596
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• 2005

The preventive diagnostic system prevents transformers from power failure by giving alarm and observing transformers in service. And it helps to establish the plan for optimum maintenance of transformer as well as to find location or cause of fault using accumulated data. KEPCO has adopted the preventive diagnostic system at nine 345kV substations since 1997. Techniques for component sensors of preventive diagnostic system were settled but diagnostic algorithm, diagnostic criteria and practical use of accumulated data are not yet established. This paper, to build up the base of preventive diagnostic algorithm for the power transformer, investigated the preventive diagnostic criteria for power transformer.