• Journal of Power System Engineering
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• v.17 no.4
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• pp.17-22
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• 2013

Diesel generator of nuclear power plant has a role for supply of emergency electric power to protect reactor core system in event of loss of off-site power supply. Therefore diesel generator should be tested periodically to verify the function that can supply specified frequency and voltage at design power level within limited time. For this purpose, appropriate maintenances in case that abnormal conditions were found are required in allowed time. In this paper, results of development of engine condition diagnosis technology and study on power plant of its technology for diesel generator are described.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2017.05a
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• pp.1165-1168
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• 2017

This paper suggests a fault diagnosis of damage on inner liner of regeneratively-cooled combustion chamber during gas generator cycle rocket engine hot firing test. This method focuses on a phenomenon that fuel flow rate difference between two flow estimate methods changes under an inner liner damage of combustion chamber causing fuel leakage and it is expected that it contributes to detect a damage on the combustion chamber in early stage and prevent further destruction during the hot firing test.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2017.05a
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• pp.218-223
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• 2017

This paper deals with a fault diagnosis algorithm using the Kalman filter for a 75-ton Liquid Propellant Rocket Engine (LPRE). To design the Kalman filter, we linearized a non-linear simulation model of a 75-ton LPRE at an operating point, and checked the performance of the Kalman filter by comparing the measured values with estimated values of the states. Then, we artificially injected a fault of the turbopump efficiency into the simulation to confirm the performance of the fault diagnosis algorithm with the developed Kalman filter by comparing the variation of the residuals of the normal state with that of the fault cases.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.3
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• pp.271-278
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• 2015

This paper develops an OBD Diagnostic Program (Program) using Visual Studio (C#), which was used to diagnosis malfunction information from OBD-II system vehicles. We accomplished this using the Program, Diagnostic tests, Board (STN1110), FTDI Basic Cable, Mini USB Cable, OBD Data Cable, and both hybrid and regular vehicles. The Program tests real-time data output, DTC output, sensor value output, engine RPM, waveform data, OBD type check, PID inspection, and whole monitoring. We found vehicles used in this research had 19 PIDs, which was within OBD-II regulations. We also gathered data on control and diagnostic code regulated by OBD-II system, such as, sensor output value, engine RPM, DTC output, each PID analytic value, OBD type, fuel mode, and whole monitoring result value. Using the data collected through the Program appropriately can lead to more effective diagnostic practices and contribute to education.

• Journal of Magnetics
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• v.21 no.2
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• pp.249-254
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• 2016

Recently, electric vehicles have got significant attention because it is more eco-friendly and efficient than internal combustion engine vehicles. Instead of an internal combustion engine, the electric vehicle has a motor for propulsion. The permanent magnet synchronous motor which has permanent magnet instead of field winding in the rotor has especially higher efficiency and power density than other types of motor. When the irreversible demagnetization is occurred, drivers are exposed to high risk of accident by the fault operation of motor. Therefore, the irreversible demagnetization of permanent magnet should be detected to reduce the risk of accident. In this study, the demagnetization diagnosis method based on the result of locked rotor test is proposed. Based on short measurement time, the proposed diagnosis method aims to detect the demagnetization fault when an electric vehicle is at a complete standstill. The proposed method is verified through the finite element analysis.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.49-53
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• 2008

A fuzzy inference logic system is proposed for gas turbine engine fault isolation. The gas path measurements used for fault isolation are exhaust gas temperature, low and high rotor speed, and fuel flow. The fuzzy inference logic uses rules developed from a model of performance influence coefficients to isolate engine faults while accounting for uncertainty in gas path measurements. Inputs to the fuzzy inference logic system are measurement deviations of gas path parameters which are transferred directly from the ECM(Engine Control Monitoring) program and outputs are engine module faults. The proposed fuzzy inference logic system is tested using simulated data developed from the ECM trend plot reports and the results show that the proposed fuzzy inference logic system isolates module faults with high accuracy rate in the environment of high level of uncertainty.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.8
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• pp.965-970
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• 2011

We develop a diagnostic system to monitor failures in an engine-assembly line. Existing techniques such as sensory analysis, time domain analysis, frequency analysis, and statistical analysis have limitations in the diagnosis of engine-assembly failure when there are abnormal vibration waveforms (crashing and damping signals) during the assembly. We use a wavelet technique to deal with crashing and damping signals. We also implement a new technique for developing diagnostic rules from sensor data, and we demonstrate its validity.

• International Journal of Automotive Technology
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• v.8 no.5
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• pp.637-650
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• 2007

A new on-line fault detection and isolation(FDI) scheme has been proposed for engines using an adaptive neural network classifier; this paper investigates the robustness of this scheme by evaluating in a wide range of operational modes. The neural classifier is made adaptive to cope with the significant parameter uncertainty, disturbances, and environmental changes. The developed scheme is capable of diagnosing faults in the on-line mode and can be directly implemented in an on-board diagnosis system(hardware). The robustness of the FDI for the closed-loop system with crankshaft speed feedback is investigated by testing it for a wide range of operational modes, including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all changes occurring simultaneously. The evaluations are performed using a mean value engine model(MVEM), which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.

• Journal of Korean Society for Quality Management
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• v.20 no.1
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• pp.91-100
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• 1992

A diagnosis system for naphtha reforming process has been designed and implemented using expert system building technique. The system is composed of knowledge base, inference engine, user interface, database and database interface. The concept and the method of this system may be applied to development of other systems for the reforming process.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.492-499
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• 2022

This study experimentally investigates the effect of dimensionality reduction of vibration signal on fault diagnosis of a marine engine. By using the principal component analysis, a vibration signal having the dimension of 513 is converted into a low-dimensional signal having the dimension of 1 to 15, and the variation in fault diagnosis accuracy according to the dimensionality change is observed. The vibration signal measured from a full-scale marine generator diesel engine is used, and the contribution of the dimension-reduced signal is quantitatively evaluated using two kinds of variable importance analysis algorithms which are the integrated gradients and the feature permutation methods. As a result of experimental data analysis, the accuracy of the fault diagnosis is shown to improve as the number of dimensions used increases, and when the dimension approaches 10, near-perfect fault classification accuracy is achieved. This shows that the dimension of the vibration signal can be considerably reduced without degrading fault diagnosis accuracy. In the variable importance analysis, the dimension-reduced principal components show higher contribution than the conventional statistical features, which supports the effectiveness of the dimension-reduced signals on fault diagnosis.