• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.1
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• pp.31-36
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• 2010

Collecting all failures during life cycle of vehicle is not easy way because its life cycle is normally over 10 years. Warranty period can help gathering failures data because most customers try to repair its failures during warranty period even though small failures. This warranty data, which means failures during warranty period, can be a good resource to predict initial reliability and permanence reliability. However uncertainty regarding reliability prediction remains because this data is censored. University of Wuppertal and major auto supplier developed the reliability prognosis model considering censored data and this model introduce to predict reliability estimate further "failure candidate". This paper predicts reliability of telecommunications system in vehicle using the model and describes data structure for reliability prediction.

• Journal of the Korean Society of Systems Engineering
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• v.10 no.1
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• pp.43-48
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• 2014

The door system for railway vehicles is the critical device directly influences on safety and satisfaction of passengers, Recently, electrical type of passenger door system is widely used for EMU type train instead of pneumatic type of passenger door system. The estimation of MTBF and failure rates for electrical type door system is essential. The manufacturor simply provides intrinsic reliability data for the railway operator. But actual reliability data based on operation and maintenance data is not complying with intrinsic reliability. In this study, operation and failure data associated with electrical door system were analyzed in order to determine actual MTBF and failure data. Intrinsic reliability data and service reliability data were studied to finallize much more practical and reliable actual reliability. Relax 2011 was used to predict intrinsic reliability and 217Plus model was also used to estimate of actual reliability data based on field data. Furthermore, it is necessary to keep studying on reliability prediction methodology and applying it in the field and doing research on improvement of reliability through feedback as well.

• Journal of Applied Reliability
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• v.9 no.4
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• pp.303-317
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• 2009

There are a lot of different data in a company. Some of the data can be modified to produce valuable information on reliability. In this study different types of data that can be obtained in a company are reviewed. Reliability related data that can be taken throughout the life cycle of a product are also reviewed. Developing a method of gathering all of the pertinent data from the various sources and databases and pulling them into one central location is explained.

• The Journal of Information Technology and Database
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• v.6 no.1
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• pp.15-25
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• 1999

We propose an extended relational data model which can represent the reliability of data. In this paper, the reliability of data is defined as the reliability of the source, from which the data originated. We represent the reliability of data at the level of attribute values, instead of tuples, then define the selection, product and join operators.

• Journal of Applied Reliability
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• v.9 no.1
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• pp.59-69
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• 2009

LEDs have rapidly replaced old light devices such as incandescent or fluorescent lamps, and have been widely applied in general lighting, signals, automobile, signs and others. Since LEDs are for both indoor and outdoor use, temperature and humidity inevitably affect its reliability. We explain the result of the degradation life test on LEDs, and guide to reliability analysis procedure. Analysis on reliability measures are performed by Weibull++6 program, and a common shape parameter of Weibull distribution on the LED is suggested. Also, we make a description of reliability analysis procedures for the degradation data using collected test data from degradation tests. Reliability analysis procedures are consisted of estimating degradation models and failure time, verifying of distribution and parameters of the distribution, and estimating of reliability measures. Finally, this paper suggests reliability analysis method for light characteristics on LEDs.

• Journal of Applied Reliability
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• v.17 no.2
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• pp.83-91
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• 2017

Purpose: The purpose of this study is to develop a quality assurance model and to determine appropriate warranty period for a guided missile using its field data. Methods: 10 years of actual firing data is collected from the defense industry company and military. Parametric maximum likelihood estimation for a reliability function is determined with the data. Results: The reliability function estimates average lifetime of the missile. That function shows a user requirement, 80% reliability (lifetime) is come up when 8 years have passed, which is longer than the estimates in the missile's development phase. Conclusion: Quality assurance warranty for a guided missile must be established with actual test data. It is necessary to update and modify the reliability prediction and the warranty period with actual field test data.

• Journal of Applied Reliability
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• v.18 no.3
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• pp.250-259
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• 2018

Purpose: Previously, missile reliability prediction is based on theoretical failure prediction model. It has shown that the predicted reliability is inadequate to real field data. Although an MTTF based reliability prediction method using real field data has recently been studied to overcome this issue. In this paper, we present a more realistic method, considering MTBF concept, to predict missile reliability. Methods: In this paper we proposed a modified survival model. This model is considering MTBF as its core concept, and failed missiles in the model are to be repaired and redeployed. We compared the modified model (MTBF) and the previous model (MTTF) in terms of fitness against the real failure data. Results: The reliability prediction result of MTBF based model is closer to fields failure data set than that of MTTF based model. Conclusion: The proposed MTBF concept is more fitted to real failure data of missile than MTTF concept. The methodology of this study can be applied to analyze field failure data of other similar missiles.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.1
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• pp.15-23
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• 2005

For reliability assessment for machine tools, failure mode analyses by two viewpoints were studied in this paper. First, this study developed the reliability data analysis program, which searches f3r optimal failure distribution like failure rate or MTBF(Mean Time Between Failure) using failure data and reliability test data of mechanical parts in the web. Moreover, this data analysis program saves both failure data or reliability data and their failure rate or MTBF for database establishment. Second, this paper conducted failure mode analysis through such performance tests as circular movement test and vibration testing for machine tools when reliability data is not available. A developed web-based analysis program shows correlations between failure mode and performance test result and also accumulates all the data. These kinds of data analysis programs and stored data furnish valuable information for improving the reliability of mechanical system.

• Journal of Applied Reliability
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• v.15 no.2
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• pp.90-100
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• 2015

Reliability growth rate (or reliability growth curve slope) have the two cases of trend as a constant or changing one during the reliability growth testing. The changing case is very common situation. The reasons of reliability growth rate changing are that the failures to follow the NHPP (None-Homogeneous Poisson Process), and the solutions implemented during test to break out other problems or not to take out all of the root cause permanently. If the changing were big, the "Goodness of Fit (GOF)" of reliability growth curve to test data would be very low and then reduce the accuracy of assessing result with test data. In this research, we are using Duane model and AMSAA model for assessing test data and projecting the reliability level of complex and repairable system as like construction equipment and vehicle. In case of no changing in reliability growth rate, it is reasonable for reliability engineer to implement the original Duane model (1964) and Crow-AMSAA model (1975) for the assessment and projection activity. However, in case of reliability growth rate changing, it is necessary to find the method to increase the "GOF" of reliability growth curves to test data. To increase GOF of reliability growth curves, it is necessary to find the proper parameter calculation method of interesting reliability growth models that are applicable to the situation of reliability growth rate changing. Since the Duane and AMSAA models have a characteristic to get more strong influence from the initial test (or failure) data than the latest one, the both models have a limitation to contain the latest test data information that is more important and better to assess test data in view of accuracy, especially when the reliability growth rate changing. The main objective of this research is to find the parameter calculation method to reflect the latest test data in the case of reliability growth rate changing. According to my experience in vehicle and construction equipment developments over 18 years, over the 90% in the total development cases are with such changing during the developing test. The objective of this research was to develop the newly assessing method and the process for GOF level increasing in case of reliability growth rate changing that would contribute to achieve more accurate assessing and projecting result. We also developed the new evaluation method for GOF that are applicable to the both models as Duane and AMSAA, so it is possible to compare it between models and check the effectiveness of new parameter calculation methods in any interesting situation. These research results can reduce the decision error for development process and business control with the accurately assessing and projecting result.

• Journal of Applied Reliability
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• v.6 no.2
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• pp.115-134
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• 2006

This paper deals with OO aircraft mission reliability prediction. To demonstrate user-required mission reliability, it is calculated with use general formulae which are used in reliability engineering. The mission reliability of OO aircraft is calculated in considering conversion factor (CF) on the each subsystems' MTBF. The prediction results are explained only the state at present time. Because these data are not real data in operational environments. Therefore, in the case of OO aircraft, it has to be needed collecting the real and renewal data which are operational and empirical. After that, continuing the data upgrading, it is easily closed to the more exact reliability value.