• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.1
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• pp.49-54
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• 2005

Recently, the reliability are applied for many industrial products, and many products are required to guarantee in quality and in performance. The purpose of this paper is to present some of reliability prediction methodologies using failure rate database for machinery parts that are applicable to machine tools. VDI Turret, which is core component of the NC Lathe, was chosen as the target of the reliability prediction. The results of reliability prediction has shown the failure rate, MTBF(Mean Time Between Failure), and reliability of the VDI Turret. It is expected that proposed methodologies will be applicable to prediction of reliability for other components of machine tools.

• Journal of Applied Reliability
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• v.11 no.3
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• pp.305-318
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• 2011

In the statement of logistics reliability prediction methodology, all components should be managed as the analysis objectives. However, in some reliability prediction of weapon systems, fastening parts, e.g., screws, bolts and nuts, have been frequently ignored because some organizations related to weapon systems have emphasized that those parts are not significant in their failures rate and functions. In this paper, failure rates, modes, and distributions were presented to prove that fastening parts should be included in reliability prediction objectives. Also, failure rate prediction methods of fastening parts are presented and compared.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.485-486
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• 2006

• Journal of the Korean Society for Railway
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• v.9 no.4 s.35
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• pp.419-424
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• 2006

In this paper we study on a method to predict and to demonstrate the reliability of the Korea high speed train control system in quantitative point of view. For the prediction of the reliability in train control system which is composed of electronic parts, Relax Software 7.7 automation tool is employed and MIL-HDBK-217 Handbook that is a standard for the prediction of the failure rate in electronic components is used. Mean Time Between Failure (MTBF) is predicted based on the failure rate of the subsystems, State Modeling and Markov Modeling method is used to express a reliability function of the train control system composed by hardware redundancy as a function of time. We propose a Reliability Test which is performed on the level of the subsystems and Failure Report, Analysing, Correction action system which use the test operation data to prove the predicted reliability.

• Journal of Applied Reliability
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• v.16 no.2
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• pp.98-103
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• 2016

Purpose: The purpose of this paper is to get a meaningful information for improving manufacturing quality of the products before they are produced in client's manufacturing process. Methods: A variety of data mining techniques have been being used for wide range of industries from process data in manufacturing factories for quality improvement. One application of those is to get meaningful information from process data in manufacturing factories for quality improvement. In this paper, the failure rate at client's manufacturing process is predicted by using the parameters of the characteristics of the product based on PCA (Principle Component Analysis) and regression analysis. Results: Through a case study, we proposed the predicting methodology and regression model. The proposed model is verified through comparing the failure rates of actual data and the estimated value. Conclusion: This study can provide the guidance for predicting the failure rate on the manufacturing process. And the manufacturers can prevent the defects by confirming the factor which affects the failure rate.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.10a
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• pp.363-366
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• 2008

It was accomplished that failure analysis not only failure numbers but also power system components every years. and these informations help power system operation considerably. power system equipment were occurred a break down by natural phenomenon and aging but it was not able to predict this failure number. But many papers and technical repots study for each equipment failure rate and reliability evaluation methods. so this paper show a failure number prediction whole power system component using Markov theory not each component failure probability. the result present a next month system failure number prediction.

• Geomechanics and Engineering
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• v.23 no.1
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• pp.51-59
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• 2020

In this paper, the WEKA platform was used to mine and analyze measured data of floor failure depth and a prediction system of floor failure depth was developed with Java. Based on the standardization and discretization of 35-set measured data of floor failure depth in China, the grey correlation degree analysis on five factors affecting the floor failure depth was carried out. The correlation order from big to small is: mining depth, working face length, floor failure resistance, mining thickness, dip angle of coal seams. Naive Bayes model, neural network model and decision tree model were used for learning and training, and the accuracy of the confusion matrix, detailed accuracy and node error rate were analyzed. Finally, artificial neural network was concluded to be the optimal model. Based on Java language, a prediction system of floor failure depth was developed. With the easy operation in the system, the prediction from measured data and error analyses were performed for nine sets of data. The results show that the WEKA prediction formula has the smallest relative error and the best prediction effect. Besides, the applicability of WEKA prediction formula was analyzed. The results show that WEKA prediction has a better applicability under the coal seam mining depth of 110 m~550 m, dip angle of coal seams of 0°~15° and working face length of 30 m~135 m.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.99-106
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• 2010

In this paper, we present two methods for obtaining prediction intervals for the times to failure of units censored in multiple stages in a progressively censored sample from proportional hazard rate models. A numerical example and a Monte Carlo simulation study are presented to illustrate the prediction methods.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.5
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• pp.675-682
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• 2013

Reliability analysis and prediction of next failure time is critical to sustain weapon systems, concerning scheduled maintenance, spare parts replacement and maintenance interventions, etc. Since 1981, many methodology derived from various probabilistic and statistical theories has been suggested to do that activity. Nowadays, many A.I. tools have been used to support these predictions. Support Vector Regression(SVR) is a nonlinear regression technique extended from support vector machine. SVR can fit data flexibly and it has a wide variety of applications. This paper utilizes SVM and SVR with combining time series to predict the next failure time based on historical failure data. A numerical case using failure data from the military equipment is presented to demonstrate the performance of the proposed approach. Finally, the proposed approach is proved meaningful to predict next failure point and to estimate instantaneous failure rate and MTBF.

• Journal of Mechanical Science and Technology
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• v.20 no.4
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• pp.473-481
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• 2006

Predicting fatigue life by numerical methods was almost impossible in the field of rubber materials. One of the reasons is that there is not obvious fracture criteria caused by nonstandardization of material and excessively various way of mixing process. But, tearing energy as fracture factor can be applied to a rubber-like material regardless of different types of fillers, relative to other fracture factors and the crack growth process of rubber could be considered as the whole fatigue failure process by the existence of potential defects in industrial rubber components. This characteristic of fatigue failure could make it possible to predict the fatigue life of rubber components in theoretical way. FESEM photographs of the surface of industrial rubber components were analyzed for verifying the existence and distribution of potential defects. For the prediction of fatigue life, theoretical way of evaluating tearing energy for the general shape of test-piece was proposed. Also, algebraic expression for the prediction of fatigue life was derived from the rough cut growth rate equation and verified by comparing with experimental fatigue lives of dumbbell fatigue specimen in various loading condition.