• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.294-303
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• 2024

The objective of condition-based maintenance plus(CBM+) is to improve the availability and maintenance efficiency of missiles, bolstering national defense capabilities. This study proposes an application of CBM+ to enhance the reliability and the safety of missiles, which are the devices typically stored for long durations. CBM+ CBM+ does not only contribute to defense capabilities, but it also aims to reduce maintenance costs. This study focuses particularly on the dormant stage of the missile life-cycle, in which various failure modes and environmental impacts on failure mechanisms are investigated. The effectiveness of maintenance strategies and the implementation of CBM+ is evaluated using simulation data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.4
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• pp.456-462
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• 2014

In this paper, we develop a facility management system which provides fault effect and replacement alarm function of each component for supporting effective maintenance of facility. To do this, we use weighting method to each component, calculate importance of each component, and make them to hierarchy structure using bill of materials of facility. Also, we draw fault cause and fault effect of components based on failure modes effects and criticality analysis, and define criteria of severity, occurrence and detection to get risk priority number. To apply these concepts, we develop and test the facility management system to verify its practicality. In the future, we expect the developed system to apply many domains such as maintenance of ship and offshore plant.

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.11a
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• pp.315-320
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• 2006

Container crane in container terminal have a lot of parts, and a crane breakdown affects the productivity of terminal. In this paper, we decide Preventive Maintenance (PM) schedules for container crane in container terminals. We define structure of container crane using three model. Also we develop a simulation system and genetic algorithm for decision PM schedules based on failure and maintenance data collected from container terminal. We compare the work schedule with PM schedules of container crane, then regulate PM schedules using heuristic method.

• Journal of Korean Society for Quality Management
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• v.17 no.1
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• pp.1-10
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• 1989

In general, the characteristics of components which consist of multi-component system can not be the same. This paper proposes a maintenance model of multi-component system considering the characteristics of each component. In this paper, multi-component system is divided into three components-critical unit, major unit and minor unit, respectively. This paper determines the optimal replacement time of the system which minimizes total maintenance cost, optimal replacement period of major unit and initial stock quantity of minor unit within this optimal replacement time. Numerical examples are shown when the failure times of each unit have gamma distribution.

• Journal of Korean Society for Quality Management
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• v.49 no.3
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• pp.269-279
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• 2021

Purpose: The purpose of this study was to suggest weapon system specifications for requirements of Condition-Based Maintenance(CBM/CBM⁺). Methods: The military documents and case studies with regard to condition-based maintenance were reviewed. Representative Korea defense specifications of weapon system such as an aircraft, a C4ISR etc. were analyzed and investigated the level of requirement for maintainability was. Results: Condition-based maintenance was defined in both U.S. instruction and Korean directive. While deparment of defense(U.S.) provide a guidebook for CBM⁺, detailed instruction was not sufficient for Korean. Ministry of national defense(ROK) define the CBM⁺ by means of IPS element which should be developed along with the system development. The maintainability was barely included in Korean defense specifications, except for BIT(Built-in test) function. As a first step for defining the condition-based maintenance requirement in defense specification, this study suggests a standard form for data needed to acquire according to types of system, fault, failure, and so on. Conclusion: The empirical researches on CMB/CBM⁺ with domestic weapon systems are not enough, and a logic which leads the maintenance strategy to CMB/CBM⁺ is not solved. Through technical researches and institutional improvements including this study, we hope that condition-based maintenance would be fully established in the Korean defense field.

• Fire Science and Engineering
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• v.24 no.3
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• pp.45-51
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• 2010

The objective of this study to analyse the cause and pattern of failing to use in Self Contained Breathing Apparatus using by government regulation for producer and client. Regulation for producer is related to legal and institution of safety inspectiontest of SCBA. Rregulation for client is preventive maintenance. Improving fail in use of SCBA are following. First, expansion of ad hoc collection inspection and safety checking is required. Second, the strict application of the law for monitoring and auditing disposal procedure in low performance SCBA is required.

• Journal of Platform Technology
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• v.6 no.3
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• pp.47-54
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• 2018

Predictive analysis in the hardware industry can be performed at an appropriate point in time to prevent failure of production facilities and reduce management costs. This helps to perform more efficient and scientific maintenance through automation of failure analysis. Among them, predictive management aims to prevent the occurrence of anomalous state by identifying and improving the abnormal state based on the gathering, analysis, and scientific data management of facilities using information technology and constructing prediction model do. In this study, we made a fault tree through the Relex tool and analyzed the error code of the hardware to study the safety.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.51-59
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• 2022

As the weapon system's dependence on software functions increased, software became a key factor in controlling the weapon system. In addition, as software development becomes more important domestically and internationally, software verification becomes an issue. The recent defense market has recognized this point and is demanding a plan for weapon system software maintenance. In this paper, we propose a weapon system software maintenance plan using Automatic Test Equipment. The specific method is to use a simulator to check the software function and identify failure cases. This is an effective way for developers to reduce the Total Corrective Maintenance Time(TCM) of the weapon system by reducing the time it takes to identify failure cases. It has been proven that the proposed Automatic Test Equipment can achieve software maintenance and excellent Maintainability and Operational Availability compared to the existing ones.

• Journal of Aerospace System Engineering
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• v.8 no.4
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• pp.7-17
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• 2014

Objective of this paper is to introduce a new technology known as prognostics and health management (PHM) which enables a real-time life prediction for safety critical systems under extreme loading conditions. In the PHM, Bayesian framework is employed to account for uncertainties and probabilities arising in the overall process including condition monitoring, fault severity estimation and failure predictions. Three applications - aircraft fuselage crack, gearbox spall and battery capacity degradation are taken to illustrate the approach, in which the life is predicted and validated by end-of-life results. The PHM technology may allow new maintenance strategy that achieves higher degree of safety while reducing the cost in effective manner.