• Journal of Advanced Marine Engineering and Technology
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• v.27 no.1
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• pp.42-48
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• 2003

In general many engineers can diagnose the fault condition using the abnormal ones among data monitored from a diesel engine, but they don't need the system modelling or identification for the work. They check the abnormal data and the relationship and then catch the fault condition of the engine. This paper proposes the construction of a fault diagnosis engine through malfunction data gained from the data fault detection system of neural networks for diesel generator engine, and the rule inference method to induce the rule for fuzzy inference from the malfunction data of diesel engine like a site engineer with a fuzzy system. The proposed fault diagnosis system is constructed in the sense of the Malfunction Diagnosis Engine(MDE) and Hierarchy of Malfunction Hypotheses(HMH). The system is concerned with the rule reduction method of knowledge base for related data among the various interactive data.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2002.05a
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• pp.233-238
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• 2002

This paper propose the construction of fault diagnosis engine for diesel generator engine and rule inference method to induce rule for fuzzy inference from the monitored data of diesel engine. The proposed fault diagnosis system is constructed the Malfunction Diagnosis Engine(MDE) and Hierarchy of Malfunction Hypotheses(HME), It is Proposed the rule reduction method of knowledge base for concerning data among the various analog data.

• Journal of Advanced Marine Engineering and Technology
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• v.26 no.4
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• pp.493-500
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• 2002

The operational data of diesel generator engine is two kinds of data. One is interactive the other is non interactive. We can find the fault information from interactive data measured for every sampling time when the changing rate, direction and status of data are investigated in comparition with those of normal status to diagnose the fault of combustion system. The various data values of combustion system for diesel engine are not proportional to load condition. The criterion to decide the level of data value is not absolute but relative to relational data. This study proposes to compose malfunction diagnosis engine using neural networks to decide that level of data value is out of normal status with the data collected from generator engine of the ship using the commercial data mining tool. This paper investigates the real ship's operational data of diesel generator engine and confirms usefulness of fault detecting through simulations for fault detecting.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2002.05a
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• pp.245-250
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• 2002

The operational data of diesel generator engine is two kind of discrete signal and analog signal. We can find the fault information from analog data measured for every sampling time if it is invested the changing rate or direction of data. This paper propose the Malfunction Diagnosis Engine(MDE) using the commercial data mining tool and show the data Process and fault finding method with the data collected from generator engine of the ship.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.5
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• pp.124-129
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• 2010

The direct injection (DI) diesel engine has become a prime candidate for future transportation needs because of its high thermal efficiency. However, nitrogen oxides (NOx) increase in the local high temperature regions and particulate matter (PM) increases in the diffusion flame region within diesel combustion. Therefore, the demand for developing hybrid system consist of exhaust gas recirculation (EGR) and aftertreatment system as well as diesel particulate filter (DPF) or lean NOx trap (LNT) should be applied. The variation of EGR rate due to the malfunction of EGR valve can affect not only the combustion stability of engine but also the performance of aftertreatment system. In this research, 2.0 liter 4-cylinder turbocharged diesel engine was used to investigate the combustion and emission characteristics for various operating conditions with EGR. While the fuel consumption was increased with increase of EGR rate, NOx emission was improved by maximum 90% at low speed, low load operating condition. To achieve combustion stability and reliability of aftertrearment system with minimum penalty in fuel consumption and emissions, the fault diagnosis of EGR malfunction must be employed.

• 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 the Korea Institute of Military Science and Technology
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• v.24 no.4
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• pp.457-466
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• 2021

This paper describes the design of model-based fault diagnosis software to apply to the propulsion system in tracked amphibious assault vehicle which consists of an engine, a transmission, a cooling system, and two waterjets. This software includes specific functions to detect the failures regarding sensor malfunctions, mechanical malfunctions, control errors, and communication errors. This software generates the proper malfunction codes which are classified as the warning and caution. In order to validate the fault diagnosis software, the manual and automatic test are performed using the test program with 32 test cases. Test results show that the designed fault diagnosis software is reliable and effective for applying to the propulsion system.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.562-569
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• 2022

The alarm monitoring technology applied to existing operating ships manages data items such as temperature and pressure with AMS (Alarm Monitoring System) and provides an alarm to the crew should these sensing data exceed the normal level range. In addition, the maintenance of existing ships follows the Planned Maintenance System (PMS). whereby the sensing data measured from the equipment is monitored and if it surpasses the set range, maintenance is performed through an alarm, or the corresponding part is replaced in advance after being used for a certain period of time regardless of whether the target device has a malfunction or not. To secure the reliability and operational safety of ship engine operation, it is necessary to enable advanced diagnosis and prediction based on real-time condition monitoring data. To do so, comprehensive measurement of actual ship data, creation of a database, and implementation of a condition diagnosis monitoring system for condition-based predictive maintenance of auxiliary equipment and piping must take place. Furthermore, the system should enable management of auxiliary equipment and piping status information based on a responsive web, and be optimized for screen and resolution so that it can be accessed and used by various mobile devices such as smartphones as well as for viewing on a PC on board. This update cost is low, and the management method is easy. In this paper, we propose CBM (Condition Based Management) technology, for autonomous ships. This core technology is used to identify abnormal phenomena through state diagnosis and monitoring of pumps and purifiers among ship auxiliary equipment, and seawater and steam pipes among pipes. It is intended to provide performance diagnosis and failure prediction of ship auxiliary equipment and piping for convergence analysis, and to support preventive maintenance decision-making.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.10
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• pp.17-26
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• 2010

This is concerned with the technology to monitor the vehicle operation, failure and disorder by using WiBro portable device. More precisely, the technology makes it possible that the information collection device is connected to both ECU(Electronic Control Unit) which is the device for controlling engine, transmission, brake, air-bag, etc that are connected to in-vehicle network and OBD-II connector that is for data collection from various sensors. In addition, with a WiBro portable device (cell phone, PDA, PMP, UMPC, etc). equipped with a vehicle diagnostic programs, information for operation, failure and malfunction can be obtained and analyzed in real-time, and alarm is alerted when the vehicle is in abnormal status, which makes the early reactions to the status. Furthermore, the collected data can be sent through WiBro network to the server managed by the company specialized in managing the vehicles, thus the technology could help the drivers who have less knowledge about their auto-vehicles have safe and economic driving. There is always a possibility of malfunction due to various types of noise that are caused by wring-harness when the device is wired-connected. In this research, in order to overcome this problem, we propose a system configuration that can do monitoring and diagnosis with a device for collecting data from vehicle and a personal WiBro device. Also, we performed research on data acquisition and interlock for the system defined by the definition for information and data sharing platform.