• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.5
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• pp.859-870
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• 2011

The purpose of this paper is to develop reliability analysis procedures for repairable systems with interval failure time data and apply the procedures for assessing the storage reliability of a subsystem of a certain type of guided missile. In the procedures, the interval failure time data are converted to pseudo failure times using the uniform random generation method, mid-point method or equispaced intervals method. Then, such analytic trend tests as Laplace, Lewis-Robinson, Pair-wise Comparison Nonparametric tests are used to determine whether the failure process follows a renewal or non-renewal process. Monte Carlo simulation experiments are conducted to compare the three conversion methods in terms of the statistical performance for each trend test when the underlying process is homogeneous Poisson, renewal, or non-homogeneous Poisson. The simulation results show that the uniform random generation method is best among the three. These results are applied to actual field data collected for a subsystem of a certain type of guided missile to identify its failure process and to estimate its mean time to failure and annual mean repair cost.

• Journal of Applied Reliability
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• v.2 no.2
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• pp.63-83
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• 2002

Degradation data can provide more reliability information than traditional failure-time data, especially products with few or no failures. This paper is concerned with a method of estimating lifetime distribution from field data with supplementary information on degradation data and covariates. When a distribution of degradation rate obtained by follow-up study for a portion of products that survive after-warranty follows a reciprocal-Weibull or lognormal distribution. A time-to-failure distribution of the product follows Weibull or lognormal distribution, respectively. A method of estimating lifetime parameters for this kind of data and their asymptotic properties are studied. Effects of after-warranty report probability, follow-up rate, and proportion of degradation data on pseudo maximum likelihood estimators of these parameters are investigated.

• 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 Institute of Industrial Engineers
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• v.25 no.4
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• pp.510-516
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• 1999

This paper proposes methods of estimating lifetime distribution from incomplete field data under parametric regression models. Failure-record data-failure times and covariates-reported to the manufacturer can be seriously incomplete for satisfactory inference since only reported failures are recorded. This paper assumes that within-warranty data are reported with probability $P_1$ ($\leq1$) and after-warranty data are reported with Methods of obtaining pseudo and after-warranty data are reported with $P_2$ (< $P_1$). Methods of obtaining pseudo maximum likelihood estimators(PMLEs) are outlined, their asymptotic properties are studied, and specific formulas for Weibull distribution are obtained. Simulation studies are perfumed to investigate the effects of follow-up percentage on the PMLEs.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.401-410
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• 2005

In this paper, we calculate the premium rate of reliability insurance policy for brake pads for automobiles using real failure data obtained from use-condition. We try process capability analysis for the manufacturing process of brake-system. We describe the performance factors which have an effect on failure characteristics of brake pads. We also obtain the maximum likelihood estimates of shape and scale parameters of the fitted Weibull distribution for brake pads.

• Journal of the Korean Society of Safety
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• v.33 no.3
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• pp.83-91
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• 2018

This paper suggests a weibull time delay model to evaluate failure risks in FMEA(failure modes and effects analysis). Assuming three types of loss functions for delayed time in failure cause detection, the risk of each failure cause is evaluated as its occurring frequency and expected loss. Since the closed form solution of the risk metric cannot be obtained, a statistical computer software R program is used for numerical calculation. When the occurrence and detection times have a common shape parameter, though, some simple results of mathematical derivation are also available. As an enormous quantity of field data becomes available under recent progress of data acquisition system, the proposed risk metric will provide a more practical and reasonable tool for evaluating the risks of failure causes in FMEA.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.3
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• pp.43-59
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• 1995

This paper presents a method for estimating the population variability distribution of the failure parameter (failure rate or failure probability) for each failure mode considered in PSA (Probabilistic Safety Assessment). We focus on the utilization of generic estimates from various industry compendia for the estimation. The estimates are complicated statistics of failure data from plants. When the failure data referred in two or more sources are overlapped, dependency occurs among the estimates provided by the sources. This type of problem is first addressed in this paper. We propose methods based on ML-II estimation in Bayesian framework and discuss the characteristics of the proposed estimators. The proposed methods are easy to apply in real field. Numerical examples are also provided.

• 전기의세계
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• v.27 no.4
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• pp.41-44
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• 1978

Intermittent failures exercise their effects only part of the time but constitute a dominant factor for the field failures. We consider the data raliability of the partially self-checking network with which a single intermittent failure will be recovered by a rollback method. Even if the self-testingness of partially self-checking network is guranteed for a set of permanent failures, it sometimes may not be so for intermittent failures. We introduce the notion of error residual and provide the basis for calculating the data reliability. Both the duration of each intermittent failure and the occurrence interval of successive ones are assumed to be negative exponentially distributed; the convolution of the intervals is distributed according to an Erlangen distribution.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.993-1000
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• 2009

In general, a drilled shaft embedded in weathered rock has a large load bearing capacity. Therefore, most of the load tests are performed only up to the load level that confirms the pile design load capacity, and stopped much before the failure load of the pile is attained. If a reliable failure load value can be extracted from the premature load test data, it will be possible to greatly improve economic efficiency as well as pile design quality. The main purpose of this study is to propose a standard for judging the reliability of the failure load of piles that is obtained from extrapolated load test data. To this aim, eleven static load test data of load-displacement curves were obtained from testing of piles to their failures from 3 different field sites. For each load-displacement curve, loading was assumed as 25%, 50%, 60%, 70%, 80%, and 90% of the actual pile bearing capacity. The limited known data were then extrapolated using the hyperbolic function, and the failure load was re-determined for each extrapolated data by the ASCE 20-96 method (1997). Statistical analysis was performed on the reliability of the re-evaluated failure loads. The results showed that if the ratio of the maximum-available displacement to the failure-load displacement exceeds 0.6, the extrapolated failure load may be regarded as reliable, having less than a conservative 20% error on average. The applicability of the proposed standard of judgment was also verified with static load test data of driven piles.

• Wind and Structures
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• v.11 no.3
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• pp.193-208
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• 2008

This paper focuses on assessing the failure of one of the transmission towers that collapsed in Winnipeg, Canada, as a result of a microburst event. The study is conducted using a fluid-structure numerical model that was developed in-house. A major challenge in microburst-related problems is that the forces acting on a structure vary with the microburst parameters including the descending jet velocity, the diameter of the event and the relative location between the structure and the jet. The numerical model, which combines wind field data for microbursts together with a non-linear finite element formulation, is capable of predicting the progressive failure of a tower that initiates after one of its member reaches its capacity. The model is employed first to determine the microburst parameters that are likely to initiate failure of a number of critical members of the tower. Progressive failure analysis of the tower is then conducted by applying the loads associated with those critical configurations. The analysis predicts a collapse of the conductors cross-arm under a microburst reference velocity that is almost equal to the corresponding value for normal wind load that was used in the design of the structure. A similarity between the predicted modes of failure and the post event field observations was shown.