• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.21 no.9
• /
• pp.105-109
• /
• 2022

In this study, the cause of the acceleration failure of Hyundai Motor's 2.0-liter CRDi engine was analyzed. We tried to find problems through MDA (Measuring Data Analyzer) based on data such as vehicle speed, air intake, and air-fuel ratio obtained during the actual driving process. As a result, it was analyzed that the failure of the EGR valve exceeded the NOx emission standard and caused a decrease in engine output. Through this study, it is possible to reduce the time and cost of unnecessary maintenance and repair, and it is expected that a rapid cause analysis will be possible in the case of new failure diagnosis in the future.

• Proceedings of the KSME Conference
• /
• 2008.11a
• /
• pp.1349-1354
• /
• 2008

In this study, we try to find the root cause of water condensing failures in a headlamp using chemical and mechanical analysis. Through the surface inspection by OM, SEM and CT, it was found that water infiltrate into the headlamp through hotmelt adhesive debonding part caused by adhesion force degradation and poor quality. IR spectra shows that adhesion force degradation are characterized by increase of some functional group(1742, 1710, 1649, 1016). Through the ESPI measurement, it is turned out that bonding structural change by thermal expansion and degradation of adhesive can be the cause of void generation. So it is recommended that cooling passage and the bonding part should be redesigned to give a guarantee of less thermal stress and high adhesion quality.

• Journal of the Korea Safety Management & Science
• /
• v.17 no.2
• /
• pp.241-247
• /
• 2015

The purpose of the warranty data analysis can be classified into two categories. Two goals is a failure cause analysis and life prediction analysis. In this paper first, we applied multivariate analysis method that can be estimated in consideration of various factors on the failure cause warranty data. In particular, we apply the Tree model and Cox model. The advantage of the Tree is easy to interpret this result as compared to other models. In addition Cox model can quantitatively express the risk. Second, this paper proposed a multivariate life prediction model (AFT) considering a variety of factors. By applying the actual warranty data confirmed the usability.

• Journal of Korean Society for Quality Management
• /
• v.39 no.2
• /
• pp.320-328
• /
• 2011

This research proposes comprehensive models for analyzing common cause failures (CCF) due to cumulative shocks and to assess system reliability under the CCF. The proposed cumulative shock models are based on the binomial failure rate (BFR) model. Six kinds of models are proposed so as to explain diverse cumulative shock phenomena. The models are composed of the initial failure probability, shape parameter, and the total shock number. Some parameters of the proposed models can not be explicitly estimated, so we adopt the Expectation-maximization (EM) algorithm in order to obtain the maximum likelihood estimator (MLE) for the parameters. By estimating the parameters for the cumulative shock models, the system reliability with CCF can be assessed sequentially according to the number of cumulative shocks. The result can be utilizes in dynamic probabilistic safety assessment (PSA), aging studies, or risk management for nuclear power plants. Replacement or maintenance policies can also be developed based on the proposed model.

• Journal of the Korean Society of Safety
• /
• v.20 no.3 s.71
• /
• pp.19-26
• /
• 2005

There are three reasoning method in fault diagnosis process. The shallow reasoning is based on the experiential knowledge and deep reasoning is based on physical model. Hybrid reasoning is mixing two type reasoning. This study describes about fault train embodiment of screw type air compressor that is used widely in industrial facilities by using various experimental method and shallow reasoning. We investigate macroscopic failure cause of air compressor through naked eye observation and then microscopic failure cause by various experimental method. We composed fault train with fault knowledge based on empirical data and scientific data that is acquired through several experiments. It is possible to analysis system reliability and failure rate with these fault train.

• The Korean Journal of Applied Statistics
• /
• v.20 no.2
• /
• pp.357-371
• /
• 2007

For the time-to-failure data with competing risks, cumulative incidence functions (CIFs) are commonly estimated using nonparametric methods. If the cases of events due to the cause of primary interest are infrequent relative to other cause of failure, nonparametric methods may result in rather imprecise estimates for CIF. In such cases, Bryant et al. (2004) suggested to model the cause-specific hazard of primary interest parametrically, while accounting for the other modes of failure using nonparametric estimator. We represented the semiparametric cumulative incidence estimator and extended to the model of Weibull and log-normal distribution. We also conducted simulations to access the performance of the semiparametric cumulative incidence estimators and to investigate the impact of model misspecification in log-normal cause-specific hazard model.

• IE interfaces
• /
• v.25 no.4
• /
• pp.405-415
• /
• 2012

The reliability performance measures for low and high or continuous demand modes of operation of safety instrumented systems(SISs) are examined and compared by analyzing the official definitions in IEC 61508 standard. This paper also presents a status of common cause factor(CCF) models used in IEC 61508 and problems relating CCF modelling are discussed and ideas to solve these ones are suggested. An example with mixed M-out-of-N architecture is carried out to illustrate the proposed methods.

• Quality Improvement in Health Care
• /
• v.5 no.2
• /
• pp.190-201
• /
• 1998

Background : Medical equipments take a very important role in diagnosis and treatment of disease in modern medicine and effective maintenance of the equipments is a necessary to provide a good health care to the public. After developing a new QC program for effective maintenance of medical equipments and practicing it for a year, we report the results of the new program. Methods : The maintenance data of 9 equipments in 8 categories including a CT Scanner were analyzed with regard to the parts responsible for most frequent failure and cause of the failure. After learning the most frequent failure part and cause of the failure, we developed a new QC program that emphasizes preventive maintenance of the most frequent failure part. We compared the number of failure per year and active rate of each equipment before and after the adoption of the new QC program. Results : The average number of failure per year per equipment was 20.7 before and it decreased by 43% to 11.9 after adoption of the new QC program. The average active rate of the equipments was 92.6% before and it increased by 3.2% to 95.8% after adoption of the new program. Conclusions : The practice of the new QC program appears very useful as it decreased the failure rate and increased the active rate of the equipments.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.8
• /
• pp.64-70
• /
• 2018

The development of weapon systems (or components) is hindered by the number of tests due to the limited development period and cost, which reduces the scale of accumulated data related to failures. Nevertheless, because a large amount of failure data and maintenance details during the operational period are managed by computerized data, the cause of failure of weapon systems (or components) can be analyzed using the data. On the other hand, analyzing the failure and maintenance details of various weapon systems is difficult because of the variation among groups and companies, and details of the cause of failure are described as unstructured text data. Fortunately, the recent developments of big data processing technology, machine learning algorithm, and improved HW computation ability have supported major research into various methods for processing the above unstructured data. In this paper, unstructured data related to the failure / maintenance of defense weapon systems (or components) is presented by applying doc2vec, a machine learning technique, to analyze the failure cases.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.27 no.3
• /
• pp.15-23
• /
• 2019

This paper contains analyses and development processes of the requirements to detect the possible intermittent failure in an old avionics backplane. Interconnections for signal transmission between electronic components, such as Pin-to-PCB, FPCB-to-FPCB, pin-to-FPCB, and pint-to-wire, were selected as the main cause of intermittent failure by analyzing target equipment and documents. The possibility of detecting intermittent failures occurring in the target equipment is verified by physics-of-failure analyses. In order to verify the occurrence of intermittent failures and their detectability, latching continuity circuit testers were manufactured and accelerated life tests were performed by applying temperature and vibration cycle in consideration of flight conditions. Through the above process, the detection requirements for the major intermittent failure in the target avionics backplane was developed.