• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.6
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• pp.706-714
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• 2020

Currently, 90 % of the world's population breathes air with a fine dust content exceeding the World Health Organization's annual average exposure limit (10 ㎍/㎥). Global efforts have been devoted toward reducing secondary pollutants and ultra-fine dust through regulations on nitrogen oxides released over land and sea. Domestic efforts have also aimed at creating clean marine environments by reducing sulfur emissions, which are the primary cause of dust accumulation in ships, through developing and distributing environment-friendly ships. Among the technologies for reducing harmful emissions from diesel engines, electrostatic precipitator offer several advantages such as a low pressure loss, high dust collection efficiency, and NOx removal and maintenance. This study aims to increase the durability of a ship by improving equipment quality through failure mode effects analysis for the preventive maintenance of an electrostatic precipitator that was developed for reducing fine dust particles emitted from the 2,427 kW marine diesel engines in ships with a gross tonnage of 999 tons. With regard to risk priority, failure mode 241 (poor dust capture efficiency) was the highest, with an RPN of 180. It was necessary to determine the high-risk failure mode in the collecting electrode and manage it intensively. This was caused by clearance defects, owing to vibrations and consequent pin loosening. Given that pin loosening is mainly caused by vibrations generated in the hull or equipment, it is necessary to manage the position of pin loosening.

• Journal of the Korean Society of Safety
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• v.30 no.5
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• pp.108-113
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• 2015

Plastic injection moulding machine is widely used for many industrial field. It is classified into mandatory safety certification machinery in Industrial Safety and Health Act because of its high hazard. In order to prevent industrial accidents by plastic injection moulding machine, it is necessary for designer to identify hazardous factors and assess the failure modes to mitigate them. This study tabulates the failure modes of main parts of plastic injection moulding machine and how their failure has affect on the machine being considered. Failure Mode & Effect Analysis(FMEA) method has been used to assess the hazard on plastic injection moulding machine. Risk and risk priority number(RPN) has been calculated in order to estimate the hazard of failures using severity, probability and detection. Accidents caused by plastic injection moulding machine is compared with the RPN which was estimated by main regions such as injection unit, clamping unit, hydraulic and system units to find out the most dangerous region. As the results, the order of RPN is injection unit, clamping unit, hydraulic unit and system units. Barrel is the most dangerous part in the plastic injection moulding machine.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2052-2060
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• 2017

The SOLAS ship should carry at least 83 different types of equipment based on the SFI group codes and each of which consists of several to dozens of components. During ship operation, it is necessary to ensure the normal operation of such equipment, and remote maintenance is highly demanded for immediate repair in the event of a equipment fault. This study proposes to find suitable classification system and to derive database structure for remote maintenance of navigation and communication equipment. As a result of this study, the classification system of equipment should be layered into equipment type, model, and component, and main table in the database consists of FMEA, service history, case data through Q&A, Preventive Maintenance. A database was constructed for 140 navigation and communication equipment models and 750 components. In order to evaluate the practical effects, service engineer evaluated the usefulness using the cloud app.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.7-18
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• 2013

These days, the teaching and learning system has been increasing for the rapid advancement of the information technologies. We can access education systems of good quality anytime, anywhere and we can use the individually personalized teaching and learning system depending on developing the wireless communication technology and the multimedia processing technology. The more the various systems develop, the more software security systems become important. There are a lot kind of fault analysis methods to evaluate software security systems. However, the only assessment method to evaluate software security system is not enough to analysis properly on account of the various types and characteristic of software systems by progressing information technology. Therefore, this paper proposes an integrative method of Fault Tree Analysis (FTA) and Fault Modes and Effect Analysis(FMEA) to evaluate the security of e-teaching and learning system as an illustration.

• Journal of the Korean Society for Railway
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• v.14 no.6
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• pp.515-525
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• 2011

In this study, Urban maglev is applied to inductive-loop speed and position detection system for the top-level classification system for the entire system, and performed functional analysis On-board signal equipment, Wayside-signal equipment divided by the reliability, availability, maintainability, and safety through analysis of the proposed formula. RDB and by applying a system service for each device was calculated to availability, safety analysis. The PHA, FMEA, HAZOP over the Top Event of the FTA is performed by presenting the results. This also shows approach methods and relative activities for project to accomplish and ensure the system requirements.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.7 no.2
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• pp.7-13
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• 2011

Turbine journal bearings are designed to support the weight of the rotors on a hydrodynamic oil film and to provide dynamic stability to the rotor system. The life time of journal bearings is infinite theoretically because the journal bearings are separated from the shaft journal by oil film. But poor design, assembly, operation and maintenance can cause problems to the journal bearings. The FMEA(Failure Mode and Effects Analysis) results of the journal bearings show that frequent maintenance of the journal bearings can cause failures and reduction of the bearing life. Therefore, the maintenance periods and history of the journal bearings with the bearing FMEA results are reviewed in order to establish the optimized maintenance period of the journal bearing for the nuclear power plants. Consequently it is necessary to maintain a best condition of lubrication system, reject time-based maintenance and perform the condition-based maintenance of journal bearings in order to maintain optimum condition of the journal bearing.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.11
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• pp.94-101
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• 2020

In the age of industry 4.0, artificial intelligence is being widely used to realize machinery condition monitoring. Due to their excellent performance and the ability to handle large volumes of data, machine learning techniques have been applied to realize the fault diagnosis of different equipment. In this study, we performed the failure mode effect analysis (FMEA) of an aluminum electrolytic capacitor by using deep learning and big data. Several tests were performed to identify the main failure mode of the aluminum electrolytic capacitor, and it was noted that the capacitance reduced significantly over time due to overheating. To reflect the capacitance degradation behavior over time, we employed the Vanilla long short-term memory (LSTM) neural network architecture. The LSTM neural network has been demonstrated to achieve excellent long-term predictions. The prediction results and metrics of the LSTM and Vanilla LSTM models were examined and compared. The Vanilla LSTM outperformed the conventional LSTM in terms of the computational resources and time required to predict the capacitance degradation.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.1
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• pp.67-73
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• 2014

A reliability evaluation or prediction can be defined as MTBF which stands for mean time between failures (Exclusively for repairable failures). Spindle system has huge effect on performance of machine tools and working quality as well as is required of high reliability. Especially, it takes great importance in producing automobiles which includes a large number of working processes. However, it is unusually difficult to predict reliability because there are lack of data and research about reliability of spindle system. Standards and methods of examinations for reliability evaluation of machine tools are scarce at local and abroad as well. Therefore, this research is meant to improve the reliability of spindle system before mass produced with developing standards of reliability and methods of examinations through FMEA to assess reliability of spindle system in prototype stages of developing high speed spindle system of machining center.

• Journal of the Korea Safety Management & Science
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• v.16 no.4
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• pp.229-236
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• 2014

Failure modes and effects analysis (FMEA) is a widely used engineering tool in the fields of the design of a product or a process to improve its quality or performance by prioritizing potential failure modes in terms of three risk factors-severity, occurrence, and detection. In a classical FMEA, the risk priority number is obtained by multiplying the three values in 10 score scales which are evaluated for the three risk factors. However, the drawbacks of the classical FMEA have been mentioned by many previous researchers. As a way to overcome these difficulties, this paper suggests the ELECTRE III that is a representative technique among outranking models. Furthermore, fuzzy linguistic variables are included to deal with ambiguous and imperfect evaluation process. In addition, when the importances for the three risk factors are obtained, the entropy method is applied. The numerical example which was previously studied by Kutlu and Ekmekio$\breve{g}$lu(2012), who suggested the fuzzy TOPSIS method along with fuzzy AHP, is also adopted so as to be compared with the results of their research. Finally, after comparing the results of this study with that of Kutlu and Ekmekio$\breve{g}$lu(2012), further possible researches are mentioned.

• Transactions of The Korea Fluid Power Systems Society
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• v.2 no.2
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• pp.1-7
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• 2005

For the reliability evaluation of the track drive unit(TDU), firstly, we analyzed the major failure modes through FMEA(failure mode & effects analysis), FTA(failure tree analysis), and 2-stage QFD(quality function deployment), and then quantitatively determined the priority order of test items. The Minitab analysis was also performed for prediction of life distribution and parameters of TDU by use of field failure data collected from 430 excavators for two years. In addition, we converted the fluctuation load in field conditions into the equivalent load, and for evaluation of the accelerated lift by the cumulative fatigues, the equivalent load is again divided into the fluctuation load by reference of test time. And then, by use of the test method in this paper, the acceleration factor(AF) of needle bearing inside planetary gear which is the most weakly designed part of TDU is achieved as 5.3. This paper presents the quantitative selection method of test items for reliability evaluation, the determination method of the accelerated life test time, and the method of non-failure test time based on a few of samples. And, we proved the propriety of the proposed methods by experiments using a TDU for a 30 ton excavator.