• Journal of KIISE:Software and Applications
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• v.27 no.4
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• pp.393-401
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• 2000

This paper explores neuro-fuzzy system in order to improve the software reliability predictability from failure data. We perform numerical simulations for actual 10 failure count and 4 failure time data sets from different software projects with the various number of rules. Comparative results for next-step prediction problem is presented to show the prediction ability of the neuro-fuzzy system. Experimental results show that neuro-fuzzy system is adapt well across different software projects. Also, performance of neuro-fuzzy system is favorably with the other well-known neural networks and statistical SRGMs.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.142-148
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• 2015

Due to advancements in technology and manufacturing capability, it is not uncommon that life tests yield no or few failures at low stress levels. In these situations it is difficult to analyse lifetime data and make meaningful inferences about product or system reliability. For some products or systems whose performance characteristics degrade over time, a failure is said to have occurred when a performance characteristic crosses a critical threshold. The measurements of the degradation characteristic contain much useful and credible information about product or system reliability. Degradation measurements of the performance characteristics of an unfailed unit at different times can directly relate reliability measures to physical characteristics. Reliability prediction based on physical performance measures can be an efficient and alternative method to estimate for some highly reliable parts or systems. If the degradation process and the distance between the last measurement and a specified threshold can be established, the remaining useful life is predicted in advance. In turn, this prediction leads to just in time maintenance decision to protect systems. In this paper, we describe techniques for mapping product or system which has degrading performance parameter to the associated classical reliability measures in the performance domain. This paper described a general modeling and analysis procedure for reliability prediction based on one dominant degradation performance characteristic considering pseudo degradation performance life trend model. This pseudo degradation trend model is based on probability modeling of a failure mechanism degradation trend and comparison of a projected distribution to pre-defined critical soft failure point in time or cycle.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2076-2085
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• 2000

Almost all existing software reliability models are based on the assumptions of he software usage and software failure process. There, therefore, is no universally applicable software reliability model. To develop a universal software reliability model this paper suggests the predictive filter as a general software reliability prediction model for time domain failure data. Its usefulness is empirically verified by analyzing the failure datasets obtained from 14 different software projects. Based on the average relative prediction error, the suggested predictive filter is compared with other well-known neural network models and statistical software reliability growth models. Experimental results show that the predictive filter generally results in a simple model and adapts well across different software projects.

• Journal of Digital Convergence
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• v.13 no.12
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• pp.143-149
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• 2015

This study aims to analyze the predict capability of some of the popular software NHPP reliability models(Goel-Okumo model, delayed S-shaped reliability model and Rayleigh distribution model). The predict capability analysis will be on two key factors, one pertaining to the degree of fitment on available failure data and the other for its prediction capability. Estimation of parameters for each model was used maximum likelihood estimation using first 80% of the failure data. Comparison of predict capability of models selected by validating against the last 20% of the available failure data. Through this study, findings can be used as priori information for the administrator to analyze the failure of software.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.535-541
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• 2007

The Non-homogeneous Poisson process is probably the most popular model since it can model systems that are deteriorating or improving. The renewal process is a model that is often used to describe the random occurrence of events in time. But both these models are based on too restrictive assumptions on the effect of the repair action. The Modulated Power Law Process is a suitable model for describing the failure pattern of repairable systems when both renewal-type behavior and time trend are present. In this paper we propose maximum likelihood estimation of the next failure time after the system has experienced some failures, that is, Mean Time Between Failure for the MPLP model.

• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3821-3828
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• 2000

Many software projects collect grouped failure data (failures in some failure interval or in variable time interval) rather than individual failure times or failure count data during the testing or operational phase. This paper presents the neural network (NN) modeling that is dble to predict cumulative failures in the variable future time for grouped failure data. ANN's predictive ability can be affected by what it learns and in its ledming sequence. Eleven training regimes that represents the input-output of NN are considered. The best training regimes dre selected rJdsed on the next' step dvemge reldtive prediction error (AE) and normalized AE (NAE). The suggested NN models are compared with other well-known KN models and statistical software reliability growth models (SHGlvls) in order to evaluate performance, Experimental results show that the NN model with variable time interval information is necessary in order to predict cumulative failures in the variable future time interval.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.1
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• pp.71-78
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• 1993

A Baysian approach is proposed is estimating the mission failure rates by criticalities. A mission failure which occurs according to a Poisson process with unknown rate is assumed to be classified as one of the criticality levels with an unknown probability. We employ the Gamma prior for the mission failure rate and the Dirichlet prior for the criticality probabilities. Posterior distributions of the mission rates by criticalities and predictive distributions of the time to failure are derived.

• Proceedings of the KSR Conference
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• 2001.05a
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• pp.347-358
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• 2001

In this paper, we apply NHPP model example to s/w process in order to get to know s/w reliability. The test is constructed by a test zig of commercial product loaded real embedded system s/w. It is established to s/w reliability prediction and estimation of real-time embedded system s/w. It is computed the prediction value of cumulative failures, the failure intensity, the reliability and the estimation value of MTTF, Failure Rate. To the more realization of high reliability in the real-time embedded system s/w, if the embedded system s/w is ensured to the test coverage and constructed to stable s/w process ＆ operating system, we can improve the performance and the reliability characteristic of the real-time embedded system s/w.

• Corrosion Science and Technology
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• v.4 no.4
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• pp.147-154
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• 2005

The creep resistance of geogrids is one of the most significant long-term safety characteristics used as the reinforcement in slopes and embankments. The failure of geogrids is defined as creep strain greater than 10%. In this study, the accelerated creep tests were applied to polyester geogrids at various loading levels of 30, 50% of the yield strengths and temperatures using newly designed test equipment. Also, the new test equipment permitted the creep testing at or above glass transition temperature($T_g$) of 75, 80, $85^{\circ}C$. The time-dependent creep behaviors were observed at various temperatures and loading levels. And then the creep curves were shifted and superposed in the time axis by applying time-temperature supposition principles. The shifting factors(AFs) were obtained using WLF equation. In predicting the lifetimes of geogrids, the underlying distribution for failure times were determined based on identification of the failure mechanism. The results confirmed that the failure distribution of geogrids followed Weibull distribution with increasing failure rate and the lifetimes of geogrids were close to 100 years which was required service life in the field with 1.75 of reduction factor of safety. Using the newly designed equipment, the creep test of geogrids was found to be highly accelerated. Furthermore, the time-temperature superposition with the newly designed test equipment was shown to be effective in predicting the lifetimes of geogrids with shorter test times and can be applied to the other geosynthetics.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4134-4145
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• 2023

This paper proposes strain-based failure model of A533B1 pressure vessel steel to simulate failure, followed by application to OECD lower head failure (OLHF) test simulation for experimental validation. The proposed strain-based failure model uses simple constant and linear functions based on physical failure modes with the critical strain value determined either using the lower bound of true fracture strain or using the average value of total elongation depending on the temperature. Application to OECD Lower Head Failure (OLHF) tests shows that progressive deformation, failure time and failure location can be well predicted.