• 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.

• 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.

• 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.

• 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.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2010.05a
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• pp.295-299
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• 2010

Recently, development and application of the condition monitoring and diagnostic system for improvement of durability and reliability and reduction of operating cost is generalized in the aircraft propulsion system. Especially, for reliable operation of the UAV which is flying in high altitude more than 40,000 ft for a long time an condition monitoring system to identify faults and degradations of its propulsion system should be needed. Therefore, this work proposes an on-line condition monitoring program using MATLAB/SIMULINK. In the development phase of the program, a engine signal generation module is used to simulate real engine measuring parameters instead of the real engine. The proposed on-line condition monitoring program was applied to a real turboprop engine to validate its application capability.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.3
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• pp.51-57
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• 2004

An intelligent performance diagnostic computer program of a gas turbine using the NN(Neural Network) was developed. Recently on-condition performance monitoring of major gas path components using the GPA(Gas Path Analysis) method has been performed in analyzing of engine faults. However because the types and severities of engine faults are various and complex, it is not easy that all fault conditions of the engine would be monitored only by the GPA approach Therefore in order to solve this problem, application of using the NNs for learning and diagnosis would be required. Among then, a BPN (Back Propagation Neural Network) with one hidden layer, which can use an updating learning rate, was proposed for diagnostics of PT6A-62 turboprop engine in this work.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.8
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• pp.541-550
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• 2022

An effective real time fault diagnosis approach for UAV engine is drawn from IMM filter and GLRT methods. For this purpose based on the linear diagnosis model derived from engine dynamic performance analysis the Kalman filter for residual estimation and each method are applied to the fault diagosis of the actuator for engine control sensors. From the process of the IMM filter application the effective FDI measure is obtained and the state responses due to actuator fault are estimated. Likewise from the GLRT method the fault magnitudes of actuator and sensors are estimated associated with some FDI functionings. The numerical simulations verify the effectiveness of the IMM filter for FDI and the GLRT in estimating the fault magnitudes of each fault mode.

• Journal of the Korean Society of Propulsion Engineers
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• v.15 no.3
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• pp.86-93
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• 2011

Recently, development and application of condition monitoring and diagnostic system for improvement of durability and reliability and reduction of operating cost is generalized in the aircraft propulsion system. Expecially, for reliable operation of the high altitude and a long time and condition monitoring system to identify faults and degradations of its propulsion system should be needed. This work proposed a GUI-based On-line condition monitoring program using LabVIEW by PT6A-67 turboprop engine. The proposed on-line condition program can monitor the real engine performance as well as the trend through precise comparison between performance results calculated by the base performance simulation program and measuring engine performance signals. In the development phase of this monitoring system, a signal generation module is proposed to evaluate the proposed on-line monitoring system.

• Journal of the Korean Society of Propulsion Engineers
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• v.11 no.1
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• pp.43-50
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• 2007

In this study, Hybrid Separate Learning Algorithm(SLA) consisting of Support Vector Machine(SVM) and Artificial Neural Network(ANN) has been used for developing the defect diagnostic algorithm of the aircraft turbo-shaft engine in the off-design range considering altitude variation. Although the number of teaming data and test data highly increases more than 6 times compared with those required for the design condition, the proposed defect diagnostics of gas turbine engine using SLA was verified to give the high defect classification accuracy in the off-design range considering altitude variation.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.4
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• pp.311-320
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• 2021

A study is performed for the real time fault diagnosis during operation and health estimation relating to performance deterioration in a turbojet engine used for an unmanned air vehicle. For this study the real time dynamic model is derived from the transient thermodynamic gas path analysis. For real fault conditions which are manipulated for the simulation, the detection techniques are applied such as Kalman filter and probabilistic decision-making approach based on statistical hypothesis test. Thereby the effectiveness is verified by showing good fault detection and isolation performances. For the health estimation with measurement parameters, it shows using an assumed performance degradation that the method by adaptive Kalman filter is feasible in practice for a condition based diagnosis and maintenance.