• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.6
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• pp.797-804
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• 2013

In this study, the authors develop a methodology for a diagnostic system with a vibration parameter that is influenced by environmental factors. The data tends to have a varying average over time. Often, these features are found in statistical data retrieved from a production line. If we utilize existing statistical techniques for these features, we could derive an incorrect diagnostic conclusion based on the different average values. To overcome the limitations of previous methods, the authors apply a function analyzed through regression analysis to predict the mean value and corresponding upper and lower limits at each stage. This technique also provides corresponding statistical parameters in varying dynamic means. To validate the proposed methods, we retrieve data from the engine assembly line of H Motors and verify the results.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2007.11a
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• pp.345-349
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• 2007

In this study a fuzzy trend monitoring method for detecting the engine mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration. etc. Using engine condition data set as a input which generated by linear regression analysis of real engine instrument data, an application of fuzzy logic in diagnostics estimate a cause of fault in each components.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.05a
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• pp.230-231
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• 2019

This study is a development on condition based maintenance(CBM) technology which is a core item of future autonomous ships. It is developing to design & installation of condition monitoring system and acquisition & processing of data from ongoing ships for fault prediction & prognosis of engine in operation. The ultimate goal of this study is to develop a predicts and decision support software for marine engine faults. To do this, the FMEA and fault tree analysis of the main engine should be accompanied by the analysis of classification of system, identification of the components, the type of faults, and the cause and phenomenon of the failure. Finally, the CBM system solution software could predict and diagnose the failure of main engine through integrated analysis for bid-data of ongoing ships and engineering knowledge. Through this study, it is possible to pro-actively cope with abnormal signals of engine and to manage efficiently, and as a result, expected that marine accident and ship operation loss during navigation will be prevented in advance.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.11a
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• pp.281-284
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute). The measurement parameters of Smart UAV propulsion system are gas generator rotational speed, power turbine rotational speed, exhaust gas temperature and torque. But two measurement such as compressor exit pressure and compressor turbine exit temperature were added because they were difficult each component diagnostics using the default measurement parameter. The performance parameters for the estimate of component performance degradation degree are flow capacities and efficiencies for compressor, compressor turbine and power turbine. Database for network learning and test was constructed using a gas turbine performance simulation program. From application results for diagnostics of the PW206C turboshaft engine using the learned networks, it was confirmed that the proposed diagnostics could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2007.11a
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• pp.350-353
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• 2007

In this study, the genetic algorithm has been used for the real-time defect diagnosis on the operation of the aircraft gas-turbine engine. The component elements of the gas-turbine engine for consideriation of the performance deterioration is consist of the compressor, the gas generation turbine and the power turbine, repectively. Compared to the on-design point on the sea-level condition, the learning data has been increased 200 times in case of the off-design conditions for the altitude, the flight mach number and the fuel consumption. Therefore, enormous learning time has been required for the satisfied convergence. The optimum division has been proposed to decrease learning time as well as to obtain high accuracy. As results, the RMS errors of the defect diagnosis using the genetic algorithm have been estimated under 5 %.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.203-210
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• 2019

Most cars today use electronic control to control the state of the engine to achieve optimum performance. This study developed a device for maintaining fault diagnosis and optimal vehicle status by displaying the engine information of a car on the screen in real time using can communication. This system displays information generated from the engine to the driver in real time such as engine intake and exhaust temperature, current battery voltage, tire pressure, RPM, DPF collection amount, torque, and horsepower through the OBD2 socket. You can check immediately. It can help you to drive safely by measuring tire pressure and displaying it on the screen, and it provides a mode to set the shift timing to suit your taste. In particular, in the case of diesel engine cars, the problems caused by smoke can adversely affect the performance and environmental pollution. Therefore, the system was developed to display the DPF collection amount on the system screen to prevent environmental pollution and to manage the vehicle efficiently.

• Journal of the Korean Society of Propulsion Engineers
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• v.4 no.4
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• pp.87-97
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• 2000

A steady state performance simulation and diagnostics program for the turboprop engine (PT6A-62), which is the power plant of the first developed military basic trainer KT-1 in Republic of Korea, was developed. The developed steady state performance analysis program was evaluated with the performance data provided by the engine manufacturer and with analysis results of GASTURB program, which is well known for the performance simulation of gas turbines. Performance parameters were discussed to evaluate validity of the developed program at various cases such as altitude, flight velocity and part load variation. GPA(Gas Pass Analysis) allows engine performance deterioration to be identified at the module level in terms of reduction in component efficiencies and changes in mass flow. In order to find optimal instrument set to detect the physical faults such as fouling, erosion and corrosion, a gas path analysis approach is utilized. This study was performed in two cases for selection of optimal measurement parameters. One case was considered with the effect of instrument number by changing independent parameter number. The other case was performed with selection of independent parameter set. According to the analysis results, the optimal measurement parameters selected were eight dependent variables such as shaft horsepower, fuel flow rate, compressor exit pressure and temperature, compressor turbine inlet pressure and temperature and power turbine inlet pressure and temperature.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.6
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• pp.57-69
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• 2001

Expert system is one of AI area. It simulates the human's way of thinking to give solutions of problem in many applications. Most expert system consists of many components such as inference engine, knowledge base, and so on. Especially the performance of expert system depend on the control of efficiency of inference engine. Inference engine has to get features; first, if possible to minimize restrictions when it constructed the knowledge base. second, it has to serve various kinds of inferencing methods. In this paper we propose knowledge scheme for representing domain knowledge in ease, knowledge implementation technique for inferencing, and integrated knowledge-base engine with blackboard and inference engine. And we describe a expert system prototype that implemented in this paper using proposed methods, it perform diagnose about heavy industrial device. The fault diagnosis system prototype has been studied in this paper will be practical foundation in the research area of knowledge based system.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.2
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• pp.89-95
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• 2003

We consider a problem of application of analytical redundancy to engine synchronization process of spark ignition engines, which is critical to timing for every ECU process including ignition and injection. The engine synchronization process we consider here is performed using the pulse signal obtained by the revolution of crankshaft trigger wheel (CTW) coupled to crank shaft. We propose a discrete-time linear model for the signal, for which we construct FDI (Fault Detection & Isolation) system consisting residual generator and threshold based on linear observer.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 1999.04a
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• pp.10-10
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• 1999

선형 및 비선형 GPA 기법을 이용한 가스터빈 엔진의 성능예측 및 진단을 연구하고 전형적 산업용 가스터빈 엔진인 TB5000에 적용하여 최적의 계측변수를 정의하였다. 선형 GPA는 가스가 지나가는 구성품의 계측가능한 온도, 압력, 연료유량, 로터 회전수 등과 같은 종속변수와 효율, 유량과 같은 측정불가능한 독립변수의 관계 방정식을 열역학 법칙, 연속방정식, 질량 및 에너지 보존법칙, 구성품 성능곡선 등으로부터 유도하는 것이며 비선형 GPA는 독립변수와 종속변수의 비선형 관계를 충분히 고려하기 위해 선형 GPA를 반복적으로 적용하는 방법이다. 본 연구에서 반복기법은 Newton-Raphson 반복기법을 사용하였다.