• Journal of Institute of Control, Robotics and Systems
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• v.22 no.2
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• pp.147-152
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• 2016

This paper proposes a new TPC (Transmission Priority Change) algorithm which is used to diagnose failures of a CAN (Controller Area Network) based network system for the oil tank monitoring. The TPC algorithm is aimed to increase the total amount of data transmission and to minimize the latency for an urgent message by changing transmission priority. The urgency of the data transmission has been determined by the conditions of sensors. There are multiple sensors inside of the oil tank, such as temperature, valve, pressure and level sensors. When the sensors operate normally, the sensory data can be collected through the CAN network by the monitoring system. However when there is a dangerous situation or failure situation happened at a sensor, the data need to be handled quickly by the monitoring system, which is implemented by using the TPC algorithm. The effectiveness of the TPC algorithm has been verified by the real experiments. In addition, this paper introduces a method that people can figure out the condition of oil tanks and also can perform the fault diagnosis in real-time by using transmitted packet data. By applying this TPC algorithm to various industries, the convenience and reliability of multiple sensors network system can be improved.

• 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.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.2032-2033
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• 2007

There has been increasing interests of condition monitoring and diagnosis for electric equipment, which lead to the development of this system domestic and abroad. In the past, operators' interest was how to quickly repair and restore the electric equipments after failure. But, due to the North American Blackout in 2003 and the aging of equipments, users have paid attention to the condition based monitoring of electric equipment to prevent a fault like outrage. GIS-PDS("GIS Preventive and Diagnosis System") requiresa large amount of measurement data with timeliness for monitoring and analysis of real-time state of GIS(Gas Insulated Switchgear). We developed the timeliness-guaranteed communication device for GIS-PDS.

• Journal of the Korean Institute of Gas
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• v.20 no.5
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• pp.57-63
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• 2016

Though a valve is a very important part in a system to control properly, it has potential failure caused by gap from valve stem to ball connection, and abrasion, crack, fixation causing operational trouble and accidents. 64% of the valve failure in the real case was the failure of airtightness and most accidents of leakage and explosion were occurred while fixing the failure. While there are no proper safety manual, maintenance procedure and regulation for industrial valves excluding safety valve, safety management for the valves is left to the discretion of the maintenance company. Strict maintenance that removes hazards and emergency response procedure are required to prevent and reduce the loss of accident. This study suggests safety enhancement measures through valve monitoring by investigating the status of use of valve and current maintenance. Enhancement of gas safety management can be acquired by a monitoring system that provides diagnosis for valve status, monitoring real-time gas leakage, management of entire cycle lifetime that can reduce maintenance cost.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.5 s.248
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• pp.520-527
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• 2006

The design of major industrial facilities for the prevention of fatigue failure is customarily done by defining a set of transients and performing a calculation of cumulative usage factor. However, sometimes, the inherent conservatism or lack of details as well as unanticipated transients in old plant may cause maintenance problems. Even though several famous on-line monitoring and diagnosis systems have been developed world-widely, in this paper, a new system fur fatigue monitoring and life evaluation of crane is proposed to reduce customizing effort and purchasing cost. With regard to the system, at first, comprehensive operating transient data has been acquired at critical locations of crane. The real-time data were classified, by using adaptive resonance theory that is one of typical artificial neural network, into representative stress groups. Then the each classified stress pattern was mapped to calculated cumulative usage factor in accordance with ASME procedure. Thereby, promising results were obtained fur the crane and it is believed that the developed system can be applicable to other major facilities extensively.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.3
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• pp.127-135
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• 2014

On lot of ships, Ballast Water Treatment Systems(BWTS) are in use, or the newly built ship should be mandatory installation by 2017. It is IMO(International Maritime Organization) regulations. Because the vessel is in long distance, It is managed this system only in the vessel and it may occur considerable cost of maintenance when a problem arises from BWTS. We describe in this paper how to monitor treated ballast water to make sure the ballast water and BWTS on a ship is in proper condition, but before the port entrance. In addition, analysing and forecasting trend signal to prevent failure. Also we can expect reduce the cost of BWTS maintenance. We can monitor the sensor data value's change which collected through satellite from BWTS. And we can observate the ballasting and de-ballasting state of the vessel easily.

• Tribology and Lubricants
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• v.36 no.6
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• pp.307-314
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• 2020

In this paper, studies on application cases of machine condition monitoring using oil sensors are reviewed. Owing to rapid industrial advancements, maintenance strategies play a crucial role in reducing the cost of downtime and improving system reliability. Consequently, machine condition monitoring plays an important role in maintaining operation stability and extending the period of usage for various machines. Machine condition monitoring through oil analysis is an effective method for assessing a machine's condition and providing early warnings regarding a machine's breakdown or failure. Among the three prevalent methods, the online analysis method is predominantly employed because this method incorporates oil sensors in real-time and has several advantages (such as prevention of human errors). Wear debris sensors are widely employed for implementing machine condition monitoring through oil sensors. Furthermore, various types of oil sensors are used in different machines and systems. Integrated oil sensors that can measure various oil attributes by incorporating a single sensor are becoming popular. By monitoring wear debris, machine condition monitoring using oil sensors is implemented for engines, automotive transmission, tanks, armored vehicles, and construction equipment. Additionally, such monitoring systems are incorporated in aircrafts such as passenger airplanes, fighter airplanes, and helicopters. Such monitoring systems are also employed in chemical plants and power plants for managing overall safety. Furthermore, widespread application of oil condition diagnosis requires the development of diagnostic programs.

• Journal of the Korean Institute of Gas
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• v.27 no.2
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• pp.7-15
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• 2023

The electric submersible pump (ESP) has been operating in production wells around the world because of its high applicability and operational efficiency among artificial lift techniques. When operating an ESP in a reservoir, variables such as temperature, pressure, gas/oil ratio, and flow rate are factors that affect ESP performance. In particular, free gas in the production fluid is a major factor that reduces the life and operational efficiency of ESP. This study presents the flow loop system which can implement the performance and damage tests of ESP considering field operating conditions to quantitatively analyze the variables that affect ESP performance. The developed apparatus in an integrated system that can diagnose the failure and causes of ESP, and detect leak of tubing by linking ESP and tubing as one system. In this study, the flow conditions for stable operation of ESP were identified through single phase and two phase flow experiments related to evaluation for the performance of ESP. The results provide the basic data to develop the failure prediction and diagnosis program of ESP, and are expected to be used for real-time monitoring for optimal operating conditions and failure diagnosis for ESP operation.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2169-2173
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• 2003

The development project of Korean High Speed Train (KHST) was started in 1996. As a national research project, the KHST project aims for a development of the next generation prototype train that has a maximum speed of 350 km/h. The development process of prototype KHST including 7 vehicles was completed last year and currently the prototype train is on its way of test running over the test track with gradually increased speed. The prototype KHST uses the real time network called TCN (Train Communication Network) for exchanging information between various onboard control equipments. After 10 years of development and modification period, TCN was confirmed as international standard (IEC61375-1) for the electrical railway equipment train bus. In the prototype KHST, all major control devices are connected by TCN and exchange their information. Such devices include SCU (Supervisory Control Unit), ATC (Automatic Train Control), TCU (Traction Control Unit), and so forth. For each device that sends and receives data using TCN, a device has to find out whether TCN is in normal or failure state before its data exchange. And also a device must have a proper method of data validation that was received in a normal TCN state. This is a one of the major important factors for devices using network. Some misleading information can lead the entire system to a catastrophic condition. This paper briefly explains how TCN was implemented in the prototype KHST train, and also shows what kind of the fault diagnosis method was adopted for a fail safe operation of TCN system

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.15
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• pp.6663-6668
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• 2015

Background: Treatment failure in leukemia is due to either pharmacokinetic resistance or cell resistance to drugs. Materials and Methods: Gene expression of multiple drug resistance protein (MDR-1), multidrug resistance-related protein (MRP) and low resistance protein (LRP) was assessed in 45 pediatric ALL cases and 7 healthy controls by real time PCR. The expression was scored as negative, weak, moderate and strong. Results: The male female ratio of cases was 2.75:1 and the mean age was 5.2 years. Some 26/45 (58%) were in standard risk, 17/45(38%) intermediate and 2/45 (4%) in high risk categorie, 42/45 (93%) being B-ALL and recurrent translocations being noted in 5/45 (11.0%). Rapid early response (RER) at day 14 was seen in 37/45 (82.3%) and slow early response (SER) in 8/45 (17.7%) cases. Positive expression of MDR-1, LRP and MRP was noted in 14/45 (31%), 15/45 (33%) and 27/45 (60%) cases and strong expression in 3/14 (21%), 11/27 (40.7%) and 8/15 (53.3%) cases respectively. Dual or more gene positivity was noted in 17/45 (38%) cases. 46.5 % (7/15) of LRP positive cases at day 14 were in RER as compared to 100% (30/30) of LRP negative cases (p<0.05). All 8 (100%) LRP positive cases in SER had strong LRP expression (p=<0.05). Moreover, only 53.3% of LRP positive cases were in haematological remission at day 30 as compared to 100% of LRP negative cases (p=<0.05). Conclusions: Our study indicated that increased LRP expression at diagnosis in pediatric ALL predicts poor response to early treatment and hence can be used as a prognostic marker. However, larger prospective studies with longer follow up are needed, to understand the clinical relevance of drug resistance proteins.