• Proceedings of the KSME Conference
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• 2001.06c
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• pp.333-338
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• 2001

Fault tree is a widely used methodology for analyzing product reliability. The fault trees are usually constructed using the experiences of expert reliability engineers in top-down approaches and have different structures according to each expert's subjectivity. In this work it is tried to find a general method for the fault tree construction based on the function tree that is the result of product function deployment. Based on the function tree, the method has the advantage of resulting an objective fault tree since the faults are defined as the opposite concept of functions. The fault tree construction of this work consists of the following steps: 1) definition of product primary function with the viewpoints of product operation and configuration, 2) construction of functional relation chart using a grouping algorithm, 3) abstraction of functional block diagram according to operation sequences and configuration of a product, 4) construction of function tree for each viewpoint, and 5) construction of fault tree by matching the function tree and simplification of the result.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.33-39
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• 2017

A top priority in the design of underwater vehicle is to guarantee the dependability of the electric system because failure of the electrical power supply system is directly related to the life of the passengers. In this paper, we present four kinds of alternative designs to improve reliability of electrical system in underwater vehicle. To reduce the risk and to increase availability of the electrical system, we use the redundancy of the grid structure and power converter. For all design alternatives, we carry out Fault Tree Analysis. Based on the FTA result, we implement RAM simulation to compare the risk and availability for the proposed design alternatives.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8700-8706
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• 2015

The first driving device of the power bogies for the Korean high-speed railway vehicle consists of the traction motor (TM) and the motor reduction gears unit (MRU). Although TM and MRU are the mechanically integrated structures, their time between overhauls (TBO) have two separate intervals due to different technical requirements(i.e. TBO of MRU: $1.8{\times}10^6km$, TBO of TM: $2.5{\times}10^6km$). Therefore, to reduce the unnecessary number of preventive maintenances, it is important to evaluate the optimal TBO with a viewpoint of reliability-center maintenance towards cost-effective solution. In this study, derived from the field data in maintenance, fault tree analysis and failure rate of the subsystem considering criticality of the components are evaluated respectively. To minimize the conventional total maintenance cost, the same optimal TBO of the components is derived from genetic algorithm considering target reliability and improvement factor. In this algorithm, a chromosome which comprised of each individual is the minimum preventive maintenance interval. The fitness function of the individual in generation is acquired through the formulation using an inverse number of the total maintenance cost. Whereas the lowest common multiple method produces only a four percent reduction compared to what the existing method did, the optimal TBO of them using genetic algorithm is $2.25{\times}10^6$km, which is reduced to about 14% comparing the conventional method.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.99-120
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• 2010

Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.