• Journal of Applied Reliability
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• v.11 no.3
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• pp.305-318
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• 2011

In the statement of logistics reliability prediction methodology, all components should be managed as the analysis objectives. However, in some reliability prediction of weapon systems, fastening parts, e.g., screws, bolts and nuts, have been frequently ignored because some organizations related to weapon systems have emphasized that those parts are not significant in their failures rate and functions. In this paper, failure rates, modes, and distributions were presented to prove that fastening parts should be included in reliability prediction objectives. Also, failure rate prediction methods of fastening parts are presented and compared.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.261-267
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• 2010

Present maintenance of a high speed railway is after the fack maintenance that executes a task when measured value goes over threshold value except some planned maintenance. It is difficult from efficient management of maintenance human resource and equipment commitment because it is difficult to predict quantity of maintenance targets. Corrective maintenance is pushed back on the repair priority of other target to need repair and it is exceeded repair cost potentially. For safety and dependable track management because track deterioration prediction is linked directly with track's life and safety of train service, it is very important that track management be based on preventive maintenance. In this study, we propose statistics model of track quality to use track inspection data and forecast model for track deterioration prediction.

• Journal of Applied Reliability
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• v.11 no.4
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• pp.433-445
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• 2011

Duty cycle is determined as the ratio of operating time to total time. Duty cycle in reliability prediction is one of the significant factors to be considered. In duty cycle application, non-operating time failure rate has been easily ignored even though the failure rate in non-operating period has not been proved to be small enough. Ignorance of non-operating time failure rate can result in over-estimated system reliability calculation. Furthermore, utilization of duty cycle in reliability prediction has not been evaluated in its effectiveness. In order to address these problems, two reliability models, such as MIL-HDBK-217F and RIAC-HDBK-217Plus, were used to analyze non-operating time failure rate. This research has proved that applying duty cycle in 217F model is not reasonable by the quantitative comparison and analysis.

• Structural Monitoring and Maintenance
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• v.10 no.2
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• pp.175-190
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• 2023

This article aims to quantitatively predict the snowmelt in extreme cold regions, considering a combination of grayscale and neural models. The traditional non-equidistant GM(1,1) prediction model is optimized by adjusting the time-distance weight matrix, optimizing the background value of the differential equation and optimizing the initial value of the model, and using the BP neural network for the first. The adjusted ice forecast model has an accuracy of 0.984 and posterior variance and the average forecast error value is 1.46%. Compared with the GM(1,1) and BP network models, the accuracy of the prediction results has been significantly improved, and the quantitative prediction of the ice sheet is more accurate. The monitoring and maintenance of the structure by quantitative prediction model by gray models was clearly demonstrated in the model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.52
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• pp.171-180
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• 1999

Industrial equipment reliability improvement and maintenance is gaining attention as the next great opportunity for manufacturing productivity improvement. Reactive maintenance is expensive because of extensive unplanned downtime and damage to machinery. To avoid such an unplanned machine downtime, it is needed to use proactive maintenance approach by either using historical maintenance data or by sensing machine conditions. This paper discusses failure diagonosis and prediction based on the condition-based maintenance and reliability technique. Thus, by enabling such a framework, it can bring us more efficient planning and execution of maintenance to reduce costs and/or increase profits.

• 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.

• Nuclear Engineering and Technology
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• v.49 no.5
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• pp.914-919
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• 2017

While industry understands the importance of keeping equipment operational and well maintained, the importance of tracking maintenance information in reliability models is often overlooked. Prognostic models can be used to predict the failure times of critical equipment, but more often than not, these models assume that all maintenance actions are the same or do not consider maintenance at all. This study investigates the influence of integrating maintenance information on prognostic model prediction accuracy. By incorporating maintenance information to develop maintenance-dependent prognostic models, prediction accuracy was improved by more than 40% compared with traditional maintenance-independent models. This study acts as a proof of concept, showing the importance of utilizing maintenance information in modern prognostics for industrial equipment.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.5
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• pp.148-160
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• 2012

This study proposed more reasonable prediction models on compressive strength and carbonation of concrete structure and developed a more effective tunnel safety diagnosis and maintenance method through field application of the proposed prediction models. For this study, the Seoul Metro's Line 1 through Line 4 were selected as target structures because they were built more than 30 years ago and have accumulated numerous diagnosis and maintenance data for about 15 years. As a result of the analysis of compressive strength and carbonation, we were able to draw prediction models with accuracy of more than 80% and confirmed the prediction model's reliability by comparing it with the existing models. We've also confirmed field suitability of the prediction models by applying field, the average error of an estimate on compressive strength and carbonation depth was about 20%, which showed an accuracy of more than 80%. We developed a more effective maintenance method using durability prediction Map before field inspection. With the durability prediction Map, diagnostic engineers and structure managers can easily detect the vulnerable points, which might have failed to reach the standard of designed strength or have a high probability of corrosion due to carbonation, therefore, it is expected to make it possible for them to diagnose and maintain tunnels more effectively and efficiently.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.4
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• pp.119-126
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• 2021

During the development of a weapon system, the concept of maintainability is used for quantitatively predicting and analyzing the maintenance time. However, owing to the complexity of a weapon system, the standard maintenance time predicted during the system's development differs significantly from the measured time during the operation of the equipment after the system's development. According to the analysis presented in this paper, the maintenance time can be predicted by considering the system's complexity on the basis of the military specifications, and the procedure can be Part B of Procedure II and Method B of Procedure V. The maintenance work elements affected by the system complexity were identified by the analytic hierarchy process technique, and the system-complexity-reflecting weights of the maintenance work elements were calculated by the Delphi method, which involves expert surveys. Based on MIL-HDBK-470A and MIL-HDBK-472, it is going to present a Korean-style maintainability prediction method that reflects system complexity of weapons systems.

• Journal of the Korea institute for structural maintenance and inspection
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• v.5 no.1
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• pp.131-140
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• 2001

In general, the deterioration induced by the freezing and thawing cyclic in concrete structures often leads to the reduction in concrete durability by the cracking or surface spalling. If it can prediction of concrete deterioration subjected to cyclic freezing and thawing, we can rationally do the design of mix proportion in view of concrete durability and the maintenance management of concrete structures. Therefore in this study a prediction method of deterioration for concrete structures subjected to the irregular freezing and thawing is proposed from the results of accelerated laboratory freezing and thawing test using the constant temperature condition and the in-situ weathering data. Furthermore, to accurately predict the concrete deterioration, a method of modification for the effect of hydration increasing during rapid freezing and thawing test is investigated.