• Journal of the Korea Safety Management & Science
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• v.3 no.4
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• pp.65-76
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• 2001

Field data have been recorded as the time to failure or the number of failure of systems. We consider the time to failure and covariate variables in some pre-specified follow-up or warranty period. This paper aims to investigate study on the reliability estimation when some additional field data can be collected within-warranty period or after-warranty period. A various likelihood-based methods are outlined and examined for exponential or Weibull distribution.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.308-313
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• 2009

This paper presents a reliability analysis of S-bonds for AF track circuits, which detect train movement and transmit a speed control signal to the train. Field survey shows that S-bonds are exposed to very large vibrations transferred from rail, and suffer from frequent failures when they were installed on ballasted track. We collected the time-to-failure data of S-bonds from the maintenance field of Seoul metro line 2, and made a parametric approach to estimate the statistical distribution that fits the time-to-failure data. The analysis shows that S-bonds have time-to-failure characteristics described by Weibull distribution. The estimated shape parameter of Weibull distribution is 1.1, which means the distribution has constant failure rate characteristics like exponential distribution. The reliability function, hazard function, percentiles and mean lifetime are derived for maintenance support.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.5
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• pp.107-114
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• 2011

The purpose of this paper is to develop a reliability estimation process of agricultural machinery components using field failure data. Estimating the durability is a time-consuming in the product development process. Using the field data of tractor, failures for major parts are investigated and databases are developed. Accelerated life test using the stress analysis could improve Weibull B10 considerably. This estimation process is useful for preparing the design input and planning the durability target.

• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.97-112
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• 2006

FRACAS(Failure Reporting, Analysis and Corrective Action System) is intended to provide management visibility and control for reliability and maintainability improvement of hardware and associated software by timely and disciplined utilization of failure and maintenance data to generate and implement effective corrective actions to prevent failure recurrence and to simplify or reduce the maintenance tasks. This process applies to acquisition for the design, development, fabrication, test, and operation or military systems, equipment, and associated computer programs. This paper shows the FRACAS development process and developed FRACAS system for a defense equipment.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.513-520
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• 2002

The stability analysis of rock slope can be predicted using a suitable field data but it cannot be predicted unless suitable field data was taken. In this study, artificial neural networks theory is applied to predict plane failure that has a few data. It is well known that human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basis of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully In this study, error back-propagation algorithm that is one of the teaching techniques of artificial neural networks is applied to predict plane failure. In order to verify the applicability of this model, a total of 30 field data results are used. These data are used for training the artificial neural network model and compared between the predicted and the measured. The simulation results show the potentiality of utilizing the neural networks for effective safety factor prediction of plane failure. In conclusion, the well-trained artificial neural network model could be applied to predict the plane failure stability of rock slope.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.503-508
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• 2003

In this study, we peformed ahead a field geological investigation, boring investigation for slope stability analysis in large scale slope failure area. But the geological stratum was not clearly grasped, because ground was very disturbed by large scale Granite intrusion. Furthermore, the existing test data was not pertinent to the large scale Granite intrusion site like here. Therefore, various kind of field test were performed to grasp clearly for geological stratum. And the results of back analysis, various kind tests used to slope stability analysis.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.5
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• pp.327-336
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• 2016

Prediction of customer failure rates plays an important role for establishing appropriate management policies and improving the profitability for industries. For these reasons, many LCD (Liquid crystal display) manufacturing industries have attempted to construct prediction models for customer failure rates. However, most traditional models are based on the parametric approaches requiring the assumption that the data follow a certain probability distribution. To address the limitation posed by the distributional assumption underpinning traditional models, we propose using parameter-free data mining models for predicting customer failure rates. In addition, we use various information associated with product attributes and field return for more comprehensive analysis. The effectiveness and applicability of the proposed method were demonstrated with a real dataset from one of the leading LCD companies in South Korea.

• Journal of Korean Society for Quality Management
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• v.27 no.4
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• pp.241-255
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• 1999

This paper concerns Reliability Analysis System(RAS) developed by LG Electronics, Inc. for collecting, classifying, and analyzing field failure data. To develop this system, a database for the management of field failure data was built and several functions were included to analyze and assess the product reliability. Nonparametric estimation and cumulative hazard plotting techniques were applied to estimate the reliability for a specific period. This system serves not only engineers in charge of quality but also designers who wish to monitor the reliability of their own products.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.84-94
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• 2018

In recent years, the diminishing of operation and maintenance cost using advanced maintenance technology is attracting many companies' attention. Especially, the heavy machinery industry regards it as a crucial problem since a failure of heavy machinery requires high cost and long downtime. To improve the current maintenance process, the heavy machinery industry tries to develop a methodology to predict failure in advance and to find its causes using usage data. A better analysis of failure causes requires more data so that various kinds of sensor are attached to machines and abundant amount of product usage data is collected through the sensor network. However, the systemic analysis of the collected product usage data is still in its infant stage. Many previous works have focused on failure occurrence as statistical data for reliability analysis. There have been less works to apply product usage data into root cause analysis of product failure. The product usage data collected while failures occur should be considered failure cause analysis. To do this, this study proposes a methodology to apply product usage data into failure cause analysis. The proposed methodology in this study is composed of several steps to transform product usage into failure causes. Various statistical analysis combined with product usage data such as multinomial logistic regression, T-test, and so on are used for the root cause analysis. The proposed methodology is applied to field data coming from operated locomotive and the analysis result shows its effectiveness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.3
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• pp.127-134
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• 2009

RAM(Reliability, Availability, Maintainability) is important performance factor to keep combat readiness and optimize operational and maintenance cost of weapon systems. This paper discusses the method to establish RAM for combat readiness by using field failure data from similarity equipments. Operational availability is estimated from a binomial distribution function of user's operational conditions such as combat readiness preservation probability, operational rate, operational availability and total number of equipment. Reliability and maintainability is estimated from field failure data from similarity equipment to accomplish operational availability. The effectiveness of established RAM is verified through analysis of combat readiness preservation probability and mission reliability. A case study of weapon system illustrates the process of the proposed method.