• Nuclear Engineering and Technology
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• v.28 no.1
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• pp.36-43
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• 1996

In this work, a PC-based thermal performance monitoring system is developed for the nuclear power plants. The system performs real-time thermal performance monitoring and diagnosis during plant operation. Specifically, a prototype for the KORI-2 nuclear power unit is developed and examined in this work. The analysis and the fault identification of the thermal cycle of a nuclear power plant is very difficult because the system structure is highly complex and the components are very much inter-related. In this study, some major diagnostic performance parameters are selected in order to represent the thermal cycle effectively and to reduce the computing time. The Fuzzy ARTMAP, a self-organizing neural network, is used to recognize the characteristic pattern change of the performance parameters in abnormal situation. By examination, this algorithm is shown to be able to detect abnormality and to identify the fault component or the change of system operation condition successfully. For the convenience of operators, a graphical user interface is also constructed in this work.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.224-227
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• 2015

Data transmission via the car driver's tethered smart phone may have a volume-dependent billing in case car driver' phone transmits data in real-time to the remote data center. The on-board diagnosis data generated are temporary stored locally to mobile remote diagnosis application on the car driver's phone, and then transmit to the data center later when car driver connects to the Internet. To increase the easiest of using the remote vehicle application without blocking other tasks to be executing on the cloud, node.js stands as a suitable candidate for handling tasks of data storage on the cloud via mobile network. We demonstrate the effectiveness of the proposed architecture by simulating a preliminary case study of an android application responsible of real time analysis by using a vehicle-to- smart phones applications interface approach that considers the smart phones to act as a remote user which passes driver inputs and delivers output from external applications. In this paper, we propose a study on development of Remote Vehicle fault diagnostic system features web server architecture based event loop approach using node.js platform, and wireless communication to handle vehicle diagnostics data to a data center.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.8
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• pp.131-137
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• 2005

The purpose of this paper is to evaluate the axial magnetic flux measurement could be used as a tool of the condition monitoring system for the induction motor and to develope the diagnostic algerian for the electric motors. The magnetic leakage flux signal is captured by the flux coil located at the end of motor without the disturbance of the operation. And the signal is analyzed both time and frequency bases to detect the failure of the motor. Specific signature can be described in time and frequency domain for each faults of the motor. The spectrum of the signal was found more useful for the monitoring purpose. The supply voltage imbalance and tin to turn failure of the stator winding could be detected by analysing the specific sidebands of the axial flux and sideband of the rotor bar pass frequency with the high resolution spectrum. The goal of this study verity that the axial flux measurement for the induction motor is a powerful tool for the diagnostic method and develope the algorithm to detect the fault.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.3
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• pp.292-301
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• 2014

This paper describes development of distributed type independent electro-hydraulic ballast valve remote control system with diagnostic function based on fuzzy inference using redundant Modbus communication and ethernet Modbus TCP/IP. Diagnostic function estimate degradation of system components and diagnose system faults, which results in shortage of fault maintenance time and improvement of system safety. Slave devices which control each valve and master device which command, monitor and diagnose slave system are developed. Slave devices are connected to master device with redundant Modbus networks and master device is connected to ship's integrated control system with Modbus TCP/IP. Also this paper describes development of simulator to test and confirm whether developed system can be integrated with ship's integrated control and monitoring system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.11
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• pp.2446-2452
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• 2012

In order to meet the recently enhanced emission standards at home and abroad, it is necessary to develop the CRDI ECU control algorithm that users can adjust fuel injection timing and amount in response to their needs. Therefore, this study developed the simulator for knocking analysis that enables knocking discrimination and engine balance correction applicable to the ECU exclusive to the industrial CRDI engine. The purpose of this study is to provide the driver-oriented diagnostic service that enable drivers to diagnose vehicles directly by developing diagnostic devices for vehicles with ths use of the results of the developed simulator for knocing analysis according to the OBD-II standards. For this purpose, this study aims to improve the fuel efficiency of vehicles by proposing the S/W design method of the OBD-II diagnosis device that can provide real-time communcations with the use of wired system and bluetooth module as a wireless system to send and recevice automobile fault diagnosis signal and sensor output signal, and to suggest an improvement for engine efficiency by minimizing the generation of harmful exhaust gas.

• Journal of Power Electronics
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• v.18 no.5
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• pp.1567-1576
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• 2018

The reliability of grid-connected inverters can be improved by algorithms capable of diagnosing faults in LCL filters. A fault diagnostic method during inverter startup is proposed. The proposed method can accurately generate and monitor information on the peak value and the location of the peak frequency component of the step response of a damped LCL filter. To identify faults, the proposed method compares the evaluated response with the response of a healthy higher-order damped LCL filter. The frequency components in the filter voltage response are first analytically obtained in closed form, which yields the expected trends for the filter faults. In the converter controller, the frequency components in the filter voltage response are computed using an appropriately designed fast Fourier transform and compared with healthy LCL response parameters using a finite state machine, which is used to sequence the proposed startup diagnostics. The performance of the proposed method is validated by comparing analytical results with the simulation and experimental results for a three-phase grid-connected inverter with a damped LCL filter.

• Aerospace Engineering and Technology
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• v.11 no.2
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• pp.182-194
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• 2012

To develop a liquid rocket engine with high reliability and safety under constraints of limited time and budget an optimal diagnosis system for the engine needs to be developed in parallel with the development of the engine. This paper is intended to set a development direction of the diagnosis system for the liquid rocket engine through the literature survey and addresses possible engine defects, characteristics of parameters for diagnosis and diagnostic methods including real-time diagnosis, post-test/post-flight diagnosis, fault detection method, parameter circuit method and test diagnosis. In addition tasks to be performed in the design and operation phases of the engine and foreign application case of engine diagnosis are presented.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.47-53
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• 2020

There is a need for a system that provides early warning of presence and type of failure of automobile wheel bearings through the application of predictive fault analysis technologies. In this paper, we presented a sensor module mounted on a wheel bearing and a diagnostic system that collects, stores and analyzes vehicle acceleration information and vibration information from the sensor module. The developed sensor module and predictive analysis system was tested and evaluated thorough excitation test equipment and real automotive vehicle to prove the effectiveness.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.5
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• pp.413-421
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• 2012

In industry, various rotating machinery such as pumps, gas turbines, compressors, electric motors, generators are being used as an important facility. Due to the industrial development, they make high performance(high-speed, high-pressure). As a result, we need more intelligent and reliable machine condition diagnosis techniques. Diagnosis technique using hidden Markov-model is proposed for an accurate and predictable condition diagnosis of various rotating machines and also has overcame the speed limitation of time/frequency method by using compensation of the rotational speed of rotor. In addition, existing artificial intelligence method needs defect state data for fault detection. hidden Markov model can overcome this limitation by using normal state data alone to detect fault of rotational machinery. Vibration analysis of step-up gearbox for wind turbine was applied to the study to ensure the robustness of diagnostic performance about compensation of the rotational speed. To assure the performance of normal state alone method, hidden Markov model was applied to experimental torque measuring gearbox in this study.

• Journal of the Korea Safety Management & Science
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• v.22 no.1
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• pp.1-8
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• 2020

Maintenance of power distribution facilities is a significant subject in the power supplies. Fault caused by deterioration in power distribution facilities may damage the entire power distribution system. However, current methods of diagnosing power distribution facilities have been manually diagnosed by the human inspector, resulting in continuous pole accidents. In order to improve the existing diagnostic methods, a thermal image analysis model is proposed in this work. Using a thermal image technique in diagnosis field is emerging in the various engineering field due to its non-contact, safe, and highly reliable energy detection technology. Deep learning object detection algorithms are trained with thermal images of a power distribution facility in order to automatically analyze its irregular energy status, hereby efficiently preventing fault of the system. The detected object is diagnosed through a thermal intensity area analysis. The proposed model in this work resulted 82% of accuracy of detecting an actual distribution system by analyzing more than 16,000 images of its thermal images.