• Journal of Korea Multimedia Society
• /
• v.16 no.3
• /
• pp.336-341
• /
• 2013

Reliability analysis for various forms of data obtained from complicated electronic circuits is a necessary process for guaranteeing reliability of the system. Reliability assessment of a system starts from the estimation of failure function. A system can be composed of one item, but in most cases, several items are correlated to each other in one system. This study suggests an estimation method of failure function and reliabilities for infrequent failure events, by considering different form of data obtained from different systems. Estimates of failure function and reliabilities for complex systems composed of two or more items in parallel or in mixed connections can be done by further application of proposed method.

• Journal of the Korea Safety Management & Science
• /
• v.23 no.3
• /
• pp.27-33
• /
• 2021

The purpose of this study is to present a novel indicator for analyzing machine failure based on its idle time and productivity. Existing machine repair plan was limited to machine experts from its manufacturing industries. This study evaluates the repair status of machines and extracts machines that need improvement. In this study, F-RPN was calculated using the etching process data provided by the 2018 PHM Data Challenge. Each S(S: Severity), O(O: Occurence), D(D: Detection) is divided into the idle time of the machine, the number of fault data, and the failure rate, respectively. The repair status of machine is quantified through the F-RPN calculated by multiplying S, O, and D. This study conducts a case study of machine in a semiconductor etching process. The process capability index has the disadvantage of not being able to divide the values outside the range. The performance of this index declines when the manufacturing process is under control, hereby introducing F-RPN to evaluate machine status that are difficult to distinguish by process capability index.

• Journal of Drive and Control
• /
• v.16 no.3
• /
• pp.33-41
• /
• 2019

This study presents the development of a lifetime test bench for the strain wave reducer which is a precision gear reducer of the robot to realize fault diagnosis and failure prognostics. To this end, the lifetime test bench was designed to detect the vertical forward/reverse direction rotation load. Through the lifetime test bench, it is possible to apply the same load spectrum from robot working scenarios. We developed a data integration gateway for fault data collection. Through the development of dedicated software for fault diagnosis and failure prognostics, these data from vibration, noise and temperature sensors were collected and analyzed along with the operation of the lifetime evaluation.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.10 no.4
• /
• pp.19-27
• /
• 2014

Software reliability in the software development process is an important issue. Software process improvement helps in finishing with reliable software product. Infinite failure NHPP software reliability models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, proposes the reliability model with log type mean value function (Musa-Okumoto and log power model), which made out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on mean square error (MSE) and coefficient of determination($R^2$), for the sake of efficient model, was employed. Analysis of failure using real data set for the sake of proposing log type mean value function was employed. This analysis of failure data compared with log type mean value function. In order to insurance for the reliability of data, Laplace trend test was employed. In this study, the log type model is also efficient in terms of reliability because it (the coefficient of determination is 70% or more) in the field of the conventional model can be used as an alternative could be confirmed. From this paper, software developers have to consider the growth model by prior knowledge of the software to identify failure modes which can be able to help.

• Journal of Environmental Science International
• /
• v.17 no.11
• /
• pp.1295-1305
• /
• 2008

This study aims at the analyze of unsteady downstream flow due to dam failure along dam failure scenario and applied to Yeoncheon Dam which was collapsed August 1st 1999, using HEC-RAS simulation model. The boundary conditions of this unsteady flow simulation are that dam failure arrival time could be at 02:45 a.m. August 1st 1999 and failure duration time could be also 30 minutes. Downstream 19.5 km from dam site was simulated for unsteady flow analysis in terms of dam failure and non-failure cases. For the parameter calibration, observed data of Jeonkok station were used and roughness coefficient was applied to simulation model. The result of the peak discharge difference was 2,696 to $1,745\;m^3/sec$ along the downstream between dam failure and non-failure and also peak elevation of water level showed meanly 0.6m difference. Those results of these studies show that dam failure scenarios for the unknown failure time and duration were rational because most results were coincident with observed records. And also those results and procedure could suggest how and when dam failure occurs and downstream unsteady flow analyzes.

• Journal of Korea Society of Industrial Information Systems
• /
• v.28 no.6
• /
• pp.1-10
• /
• 2023

The nuclear power plant(NPP)'s Instrumentation and Control(I&C) system periodically conducts integrity checks for the maintenance of self-diagnostic function during normal operation. Additionally, it performs functionality and performance checks during planned preventive maintenance periods. However, there is a need for technological development to diagnose failures and prevent accidents in advance. In this paper, we studied methods for estimating the reliability function by utilizing environmental data and self-diagnostic data of the I&C equipment. To obtain failure data, we assumed probability distributions for component features of the I&C equipment and generated virtual failure data. Using this failure data, we estimated the reliability function using representative artificial intelligence(AI) models used in survival analysis(DeepSurve, DeepHit). And we also estimated the reliability function through the Cox regression model of the traditional semi-parametric method. We confirmed the feasibility through the residual lifetime calculations based on environmental and diagnostic data.

• International Journal of Heart Failure
• /
• v.6 no.1
• /
• pp.11-19
• /
• 2024

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

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

• Tunnel and Underground Space
• /
• v.22 no.1
• /
• pp.12-21
• /
• 2012

Although the Mohr-Coulomb and Hoek-Brown failure criteria have been adopted widely in rock mechanics, they neglect the ${\sigma}_2$ effect. The result of true triaxial tests on rock samples, however, reveals that the ${\sigma}_2$ effect on strength of rocks is considerable, so that rock failure criteria taking into account the influence of ${\sigma}_2$ are necessary for the precise stability evaluation of rock structures. In this study, a new nonlinear 3-D failure criterion has been suggested by combining the Hoek-Brown criterion with the smooth octahedral shape function taken from Jiang & Pietruszczak (1988). The performance of the new criterion was assessed by comparing the strength predictions from both the suggested criterion and the corresponding linear 3-D criterion. The resulting fit of the new criterion to the true triaxial test data for six rock types taken from the literature shows that the criterion fits the experimental data very well. Furthermore, for the data sets having data taken in the low ${\sigma}_3$ range, the nonlinear failure criterion works better than the linear criterion.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.1
• /
• pp.372-378
• /
• 2018

Life management technology is required as the failure risk of aged power transformers increases. Asset management technology is developed to evaluate the remaining life, establish the replacement strategies, and decide the optimal investment based on the reliability and economy of power transformers. The remaining life assessment uses data such as installation, operation, maintenance, refurbishment, and failure of power transformers. The optimal investment also uses data such as maintenance, outage, and social costs. To develop the asset management system for power transformers, determining the degradation parameters related to the aging of power transformers and evaluating the condition of power transformers using these parameters are important. In this study, since 1983, 110,000 Dissolved Gas Analysis (DGA) data have been analyzed to determine the degradation parameters related to the aging of power transformers. The alarm rates of combustible gases ($H_2$, $C_2H_2$, $C_2H_4$, $CH_4$, and $C_2H_6$), TCG, CO, and $CO_2$ were analyzed. The end of life and failure rate (bathtub curve) of power transformers were also calculated based on the failure data from 1981 to 2014. The DGA gases related to discharge, overheating, and insulation degradation were determined based on alarm and failure rates. $C_2H_2$, $C_2H_6$, and $CO_2$ were discharge, oxidation, and insulation degradation parameters related to the aging of power transformers.