• 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.

• IE interfaces
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• v.14 no.3
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• pp.272-285
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• 2001

Proper maintenance techniques have been emphasized in recent years due to increased safety and reliability requirements of systems, increased complexity, and rising costs of material and labor. In the related literature, most studies assume that the system after cm or pm is 'as good as new'(perfect maintenance) or 'as bad as old'(minimal maintenance). But many maintenance activities may not result in these two extreme situations but in a complicated intermediate one. Therefore, perfect maintenance and minimal maintenance are not practical in many actual instances and so realistic imperfect maintenance should be modeled. For this study, various imperfect preventive maintenance models are investigated and analyzed. From the analysis of the imperfect maintenance models modeling methods and maintenance policies are arranged and presented some tables providing informations to the maintenance managers.

• Journal of Korean Society of Steel Construction
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• v.14 no.5 s.60
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• pp.627-639
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• 2002

Bridge construction is almost complete in many countries. Thus, the government and highway agencies change their focus from constructing to maintaining. Effectively maintaining bridges require predicting their lifespan using a system reliability viewpoint. Likewise, maintenance models based on the system reliability concept should be developed. Thus, this study developed maintenance models for preventive maintenance and essential maintenance using system reliability and lifetime distributions. The optimal maintenance strategy for an existing bridge was obtained using the developed maintenance models.

• Journal of Korean Society of Water and Wastewater
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• v.28 no.6
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• pp.669-679
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• 2014

3D-based BIM(Building Information Modeling) technologies can be utilized effectively as a means of systematic management of facility information for safety assurance and effective maintenance of waterworks facilities. In this study, BIM models of water treatment facilities that can be used as basic data for BIM-based maintenance of waterworks facilities were developed. Information exchange and generality of the developed BIM models were evaluated by conducting interoperability analysis of IFC(Industry Foundation Classes) conversion models. In addition, the application of COBie(Construction Operations Building information exchange) was recommended as an effective countermeasure to deal with technical limitation regarding exchange and utilization of facilities-related information through current IFC models. The results of this study can contribute to the development of BIM-based maintenance system for waterworks facilities.

• Journal of Korean Society of Steel Construction
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• v.14 no.3
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• pp.443-452
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• 2002

Construction of highway bridges is almost complete in many countries. Thus, the government and highway agencies are shifting their remaining life span from the viewpoint of system reliability. As such, it is necessary to develop maintenance models based on system reliability concept. In this paper, preventive and essential maintenance models were developed using system reliability and lifetime distribution.

• Journal of Applied Reliability
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• v.16 no.4
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• pp.313-322
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• 2016

Purpose: We introduce ways to employ Markov chain model to evaluate the effect of preventive maintenance process. While the preventive maintenance process decreases the failure rate of each subsystems, it increases the downtime of the system because the system can not work during the maintenance process. The goal of this paper is to introduce ways to analyze this trade-off. Methods: Markov chain models are employed. We derive the availability of the system consisting of N repairable subsystems by the methods under various maintenance policies. Results: To validate our methods, we apply our models to the real maintenance data reports of military truck. The error between the model and the data was about 1%. Conclusion: The models developed in this paper fit real data well. These techniques can be applied to calculate the availability under various preventive maintenance policies.

• Journal of Applied Reliability
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• v.17 no.2
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• pp.159-167
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• 2017

Purpose: The purpose of this research is to study research trend in the field of warranty and maintenance policy of second-hand products. Methods: To this end, we consider research articles, which deal with warranty and maintenance of the second-hand products, published on journals during the past 20 years and classify them by taxonomy scheme proposed by Shafiee and Chukova (2013). The taxonomy scheme consists of three maintenance models in warranty for second-hand product. In each models, we analyze proposed maintenance and warranty policies with respect to types of upgrade models, types of preventive maintenances, decision variables and decision criteria model. Results: We obtain the scheme of maintenance and warranty of the second-hand products and define cost related to warranty and maintenance of the second-hand item. Also, we summarize the characteristics of maintenance and warranty policies in each classified model. Conclusion: There have been several research reviews on maintenance and warranty polity of new products. This research surveys researches of authors during the past 20 years and classifies, summarizes and compares proposed maintenance and warranty policies of the second-hand products. This research provides useful information to researchers who are interested in maintenance and warranty of the second-hand products.

• IE interfaces
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• v.15 no.3
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• pp.286-296
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• 2002

Products which are sold with warranty, preventive maintenance actions by manufacturers and/or buyers have an impact on the total costs for both parties. In this paper, we develop the models to study the expected warranty cost for products with free repairable warranty with three types of discrete preventive maintenance. We deal with by utilizing the concept that preventive maintenance reduces the virtual age of the system. We assume that the maintenance planning horizon can be segmented into k discrete and equally sized periods. In such a scenario, numerical examples are presented.

• 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.

• Industrial Engineering and Management Systems
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• v.7 no.1
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• pp.51-56
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• 2008

With growing demand for zero defects, predicting reliability of software systems is gaining importance. Software reliability models are used to estimate the reliability or the number of latent defects in a software product. Most reliability models to estimate the reliability of software in the literature are based on the development lifecycle stages. However, in the maintenance phase, the software needs to be corrected for errors and to be enhanced for the requests from users. These decrease the reliability of software. Software Reliability Growth Models (SRGMs) have been applied successfully to model software reliability in development phase. The software reliability in maintenance phase exhibits many types of systematic or irregular behaviors. These may include cyclic behavior as well as long-term evolutionary trends. The cyclic behavior may involve multiple periodicities and may be asymmetric in nature. In this paper, SGRM has been adapted to develop a reliability prediction model for the software in maintenance phase. The model is established using maintenance data from a commercial shop floor control system. The model is accepted to be used for resource planning and assuring the quality of the maintenance work to the user.