• Journal of the Korea Safety Management & Science
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• v.14 no.2
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• pp.147-158
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• 2012

This paper aims to develop a new chain metrics for obtaining lean Overall Equipment Effectiveness(OEE) and present implementation strategy which considers the properties for Total Productive Maintenance(TPM) to reduce machine losses, Performance Analysis and Control(PAC) to reduce labor losses, Lean Production System(LPS) to reduce floor wastes, and Theory of Constraints(TOC) to minimize the problem of Capacity Constrained Resource(CCR). The study reviews the related literatures and reformulates the structure of machine losses, labor losses and field wastes. The research also develops the integrated productivity metrics according to time, units, reliability and maintainability. It is found that the study develops the actual productivity measure in terms of efficiency, effectiveness and standard productivity. In addition to that, it outlines and develops by using the integrated LPS and TPM, lean OEE measures such as Time Based Productivity(TBP), Unit Based Productivity(UBP), and Reliability & Maintainability Based Availability(RMBA). Implication examples are proposed to make it easier and available for practioners to understand the implementation strategies about TPM OEE, lean OEE and TOC OEE. Futhermore related to other studies, the research contributes to create a new chain productivity measures to clear the interrelationship concepts of productivity, efficiency and effectiveness. Moreover the paper develops the enhanced OEE measures by integration of TPM, PAC, LPS and TOC with the perspective of schedule, throughput, reliability, maintainability and availability.

• Journal of Korean Society for Quality Management
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• v.19 no.1
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• pp.129-140
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• 1991

This paper deals with on (S-1, S) spare-part inventory model for multi-stage machine repair problem with attrition. The steady-state availability of the system is maximized under some constraints such as total cost, available space etc.. The problem is formulated as a closed queueing network and the system availability is calculated by Buzen's computational algorithm. In order to find the optimal numbers of spare units and repair channels for each operating stage, the problem is formulated as a non-linear integer programming(NLIP) problem and an efficient algorithm. which is a natural extension of the new Lawler-Bell algorithm of Sasaki et el., is used to solve the NLIP problem. A numerical example is given to illustrate the algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.766-773
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• 2021

This paper presents an implementation result and performance evaluation of pavilion management services that does not only provide real-time status of the pavilion in the forest but also prediction services through machine learning. The developed hardware prototype detects whether the pavilion is occupied using a motion detection sensor and then sends it to a cloud database along with location information, date and time, temperature, and humidity data. The real-time usage status of the collected data is provided to the user's mobile application. The performance evaluation confirms that the average response time from the hardware module to the applications was 1.9 seconds. The accuracy was 99%. In addition, we implemented a pavilion availability prediction service that applied a machine learning-based SVM (Support Vector Model) model to collected data and provided it through mobile and web applications.

• Tribology and Lubricants
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• v.18 no.4
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• pp.285-290
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• 2002

Integrated condition monitoring is required to monitor effectively the machine conditions since machine failures could not be monitored accurately by any single measurement parameter. Application of various condition monitoring techniques is therefore preferred in many cases in order to diagnosis the machine condition. However it inevitably requires lots of maintenance cost and sometimes it could be proved to over-maintenance unnecessarily. This could happen especially when one measurement parameter closely correlates to another. Therefore correlation analysis of various monitoring parameters has to be performed to improve the reliability of diagnosis. In this work, Pearson correlation coefficient was used to analyze the correlation between condition monitoring parameters of an over-loaded machine system where the vibration, wear and temperature were monitored simultaneously. The result showed that Pearson correlation coefficient could be regarded as a good measure for evaluating the availability of condition monitoring technology.

• International Journal of Fluid Machinery and Systems
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• v.2 no.1
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• pp.61-71
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• 2009

Machine fault diagnostic and prognostic techniques have been the considerable subjects of condition-based maintenance system in the recent time due to the potential advantages that could be gained from reducing downtime, decreasing maintenance costs, and increasing machine availability. For the past few years, research on machine fault diagnosis and prognosis has been developing rapidly. These publications covered in the wide range of statistical approaches to model-based approaches. With the aim of synthesizing and providing the information of these researches for researcher's community, this paper attempts to summarize and classify the recent published techniques in diagnosis and prognosis of rotating machinery. Furthermore, it also discusses the opportunities as well as the challenges for conducting advance research in the field of machine prognosis.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2001.11a
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• pp.252-259
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• 2001

Integrated condition monitoring is required to monitor effectively the machine conditions since machine failures could not be monitored accurately by any single measurement parameter. Application of various condition monitoring techniques is therefore preferred in many cases in order to diagnosis the machine condition. However it inevitably requires lots of maintenance cost and sometimes it could be proved to over-maintenance unnecessarily. This could happen especially when one measurement parameter closely correlates to another. Therefore correlation analysis of various monitoring parameters has to be performed to improve the reliability of diagnosis. In this work, Pearson correlation coefficient was used to analyze the correlation between condition monitoring parameters of an over-loaded machine system where the vibration, wear and temperature were monitored simultaneously. The result showed that Pearson correlation coefficient could be regarded as a good measure for evaluating the availability of condition monitoring technology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3169-3181
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• 2015

Nowadays, online word-of-mouth has become a powerful influencer to marketing and sales in business. Opinion mining and sentiment analysis is frequently adopted at market research and business analytics field for analyzing word-of-mouth content. However, there still remain several challengeable areas for 1) sentiment analysis aiming for Korean word-of-mouth content in film market, 2) availability of machine learning models only using linguistic features, 3) effect of the size of the feature set. This study took a sample of 10,000 movie reviews which had posted extremely negative/positive rating in a movie portal site, and conducted sentiment analysis with four machine learning algorithms: naïve Bayesian, decision tree, neural network, and support vector machines. We found neural network and support vector machine produced better accuracy than naïve Bayesian and decision tree on every size of the feature set. Besides, the performance of them was boosting with increasing of the feature set size.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.2
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• pp.35-43
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• 2020

Cloud computing integrates computing resources such as servers, storage, and networks with virtualization technology to provide suitable services according to user needs. Due to the structural characteristics of sharing physical resources based on virtualization technology, threats to availability can occur, so it is essential to respond to availability threats in cloud computing. Existing over-provisioning method is not suitable because it can generate idle resources and cause under-provisioning to degrade or disconnect service. System resources must be allocated in real-time according to the system load to guarantee the cloud system's availability. Through appropriate management measures, it is necessary to reduce the system load and increase the performance of the system. This paper analyzes the work response time according to the allocation or migration of virtual machines and discusses an efficient resource management method considering the system load.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.1
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• pp.97-104
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• 2005

Considered is the concurrent spare part(CSP) requirements problem of new equipment system. In the considered system, when a part fails, the part cannot be repaired and should be replaced. In addition, cannibalization is allowed. It is assumed that the failure of a part follows a Poisson process. The operational availability concept in CSP is defined, and a formula is derived to calculate the operational availability using expected machine operating time during CSP period. The problem is formulated as the operational availability maximization problem with available budget constraint and a heuristic solution search procedure is developed. An illustrative example is shown to explain the solution procedure.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.1
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• pp.115-121
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• 2007

In this paper, we study flowshop scheduling problems with availability constraints. In such problems, n jobs have to be scheduled on m machines sequentially under assumption that the machines are unavailable during some periods of planning horizon. The objective of the problem is to find a non-permutation schedule which minimizes the makespan. As a solution procedure, we propose an improved genetic algorithm which utilizes a look-ahead schedule generator to find good solutions in a reasonable time Computational experiments show that the proposed genetic algorithm outperforms the existing genetic algorithm.