• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4A
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• pp.647-657
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• 2006

Probabilistic Risk Assessment considering statistically random variables is performed for the preliminary design of a Cable Stayed Bridge, which is Prestressed Concrete Bridge consisted of cable and plate girders, based on the method of Working Stress Design and Strength Design. Component reliabilities of cables and girders have been evaluated using the response surface of the design variables at the selected critical sections based on the maximum shear, positive and negative moment locations. Response Surface Method (RSM) is successfully applied for reliability analyses for this relatively small probability of failure of the complex structure, which is hard to obtain through Monte-Carlo Simulations. or through First Order Second Moment Method that can not easily calculate the derivative terms of implicit limit state functions. For the analysis of system reliability, parallel resistance system consisting of cables and plate girder is changed into series connection system and the result of system reliability of total structure is presented. As a system reliability, the upper and lower probabilities of failure for the structural system have been evaluated and compared with the suggested prediction method for the combination of failure modes. The suggested prediction method for the combination of failure modes reveals the unexpected combinations of element failures in significantly reduced time and efforts compared with the previous permutation method or system reliability analysis method, which calculates upper and lower bound failure probabilities.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.18-30
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• 2022

Predicting remaining useful life (RUL) becomes significant to implement prognostics and health management of industrial systems. The relevant studies have contributed to creating RUL prediction models and validating their acceptable performance; however, they are confined to drive reasonable preventive maintenance strategies derived from and connected with such predictive models. This paper proposes a data-driven preventive maintenance method that predicts RUL of industrial systems and determines the optimal replacement time intervals to lead to cost minimization in preventive maintenance. The proposed method comprises: (1) generating RUL prediction models through learning historical process data by using machine learning techniques including random forest and extreme gradient boosting, and (2) applying the system failure time derived from the RUL prediction models to the Weibull distribution-based minimum-repair block replacement model for finding the cost-optimal block replacement time. The paper includes a case study to demonstrate the feasibility of the proposed method using an open dataset, wherein sensor data are generated and recorded from turbofan engine systems.

• Proceedings of the Korean Reliability Society Conference
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• 2006.05a
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• pp.75-75
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• 2006

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.10
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• pp.102-111
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• 2021

Maintainability is a major design parameter that includes availability as well as reliability in a RAM (reliability, availability, maintainability) analysis, and is an index that must be considered when developing a system. There is a lack of awareness of the importance of predicting and analyzing maintainability; therefore, it is dependent on past-experience data. To improve the utilization rate, maintainability must be managed as a key indicator to meet the user's requirements for failure maintenance time and to reduce life-cycle costs. To improve the maintainability-prediction accuracy in the detailed design stage, we present a maintainability-prediction method that applies Method B of the Military Standardization Handbook (MIL-HDBK-472) Procedure V, as well as a Korean maintainability-prediction software package that reflects the system complexity.

• International Journal of Railway
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• v.5 no.1
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• pp.1-9
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• 2012

Reliability means that a product maintains its initial quality and performance at a certain period of time (time, distance, cycle etc) under given condition without failure. The given conditions include both environmental condition and operating condition. Environmental condition means a common natural environment such as temperature, humidity, vibration, and working condition means an artificial environment such as voltage, current load, place for installment, and hours of use, which occurs during the life of the product. In the field of railway vehicles, it is mandatory to use a part with the proved reliability as the extension of the life of vehicle become highly necessary. But the reliable assessment method for the reliability of the part is insufficient. If the reliability of the railway vehicle parts could be assessed by using the field data, the reliability of the entire system could also be evaluated reliably. In this study, life span of micro-switch for master controller is analyzed and prediction is performed based on its field data given by an operator considering the special circumstances of railway vehicles such as the operation of a large number of trains on the same line.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.201-207
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• 2017

Failure of the power apparatus causes many inconveniences and problems due to power outage in all places using power such as industry and home. The main causes of faults in the Power Apparatus are aging, natural disasters such as typhoons and earthquakes, and animals. At present, the long high temperature status is monitored only by the assumption that a fault occurs when the temperature of the power apparatus becomes higher. Therefore, it is difficult to cope with the failure of the power apparatus at the right time. In this paper, we propose a power apparatus monitoring system as an efficient countermeasure against general faults except for faults caused by sudden natural disasters. The proposed monitoring system monitors the power apparatus in real time by attaching a thermal sensor, collects the monitored data, and predicts the failure using the accumulated information through learning using the artificial neural network. Through the learning and experimentation of artificial neural network, it is shown that the proposed method is efficient.

• Journal of the Korean Society of Safety
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• v.21 no.1 s.73
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• pp.28-34
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• 2006

A procedure of estimating failure probability is demonstrated for a pressurized pipe of CrMo steel used at $538^{\circ}C$. Probabilistic fracture mechanics were employed considering variations of pressure loading, material properties and geometry. Probability density functions of major material variables were determined by statistical analyses of implemented data obtained by previous experiments. Distributions of the major variables were reflected in Monte Carlo simulation and failure probability as a function of operating time was determined. The creep crack growth life assessed by conventional deterministic approach was shown to be conservative compared with those obtained by probabilistic one. Sensitivity analysis for each input variable was also conducted to understand the most influencing variables to the residual life analysis. Internal pressure, creep crack growth coefficient and creep coefficient were more sensitive to failure probability than other variables.

• The KIPS Transactions:PartD
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• v.8D no.2
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• pp.175-180
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• 2001

This paper presents the optimal fraction of validation set to obtain a prediction accuracy of software failure count or failure time in the future by a neuro-fuzzy system. Given a fixed amount of training data, the most popular effective approach to avoiding underfitting and overfitting is early stopping, and hence getting optimal generalization. But there is unresolved practical issues : How many data do you assign to the training and validation set\ulcorner Rules of thumb abound, the solution is acquired by trial-and-error and we spend long time in this method. For the sake of optimal fraction of validation set, the variant specific fraction for the validation set be provided. It shows that minimal fraction of the validation data set is sufficient to achieve good next-step prediction. This result can be considered as a practical guideline in a prediction of software reliability by neuro-fuzzy system.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.11a
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• pp.25-30
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• 2004

This paper gives an assessment criteria and average failure lifetime prediction for power supply of electrodeless fluorescent lamp. The paper present electric appliance safety standard and performance standard for power supply of electrodeless fluorescent lamp. also, It presents the Failure Rate or Mean Time To Failure(MTTF) for power supply of electrodeless fluorescent lamp. We suggest the assessment criteria and improve methods of the reliability on the design basis for the electrodeless fluorescent system.

• Journal of the Korean Society for Railway
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• v.9 no.6 s.37
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• pp.725-731
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• 2006

Hot Standby sparing system detecting faults by using software, and being tolerant any faults by using Hardware Redundancy is difficult to perform quantitative reliability prediction and to detect real time faults. Therefore, this paper designs Hot Standby sparing system using hardware basis self checking logic in order to overcome this problem. It also performs failure mode analysis of Hot Standby sparing system with designed self checking logic by using FMEA (Failure Mode Effect Analysis), and identifies reliability assessment of the controller designed by quantifying the numbers of failure development by using FTA (Fault Tree Analysis)