• Title/Summary/Keyword: Lifetime Prediction Model

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A Study on the Reliability Prediction and Lifetime of the Electrolytic Condenser for EMU Inverter (전동차 인버터 구동용 전해콘덴서의 신뢰도예측과 수명 연구)

  • Han, Jae-Hyun;Bae, Chang-Han;Koo, Jeong-Seo
    • Journal of the Korean Society of Safety
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    • v.29 no.1
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    • pp.7-14
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    • 2014
  • Inverter module, which feeds the converted power to the traction motor for EMU. Consists of the power semiconductors with their gate drive unit(GDU)s and the control computer for driving, voltage, current and speed controls. Electrolytic condenser, connected to the gate drive unit and a core component to drive the power semiconductor, has problems such as reduction in lifetime and malfunction caused by electrical and mechanical characteristic changes from heat generation during high speed switching for generation of stable power. In this study, To check the service life of electrolytic condenser, the test was carried out in two ways. First, In the case of accelerated life testing of condenser, the Arrhenius model is a way of life testing. Another way is to analyze the reliability of the failure data by the method of parametric data analysis. Eventually, life time by accelerated life test than a method of failure data analysis(Weibull distribution) was found to be slightly larger output.

Statistical analysis on the fluence factor of surveillance test data of Korean nuclear power plants

  • Lee, Gyeong-Geun;Kim, Min-Chul;Yoon, Ji-Hyun;Lee, Bong-Sang;Lim, Sangyeob;Kwon, Junhyun
    • Nuclear Engineering and Technology
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    • v.49 no.4
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    • pp.760-768
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    • 2017
  • The transition temperature shift (TTS) of the reactor pressure vessel materials is an important factor that determines the lifetime of a nuclear power plant. The prediction of the TTS at the end of a plant's lifespan is calculated based on the equation of Regulatory Guide 1.99 revision 2 (RG1.99/2) from the US. The fluence factor in the equation was expressed as a power function, and the exponent value was determined by the early surveillance data in the US. Recently, an advanced approach to estimate the TTS was proposed in various countries for nuclear power plants, and Korea is considering the development of a new TTS model. In this study, the TTS trend of the Korean surveillance test results was analyzed using a nonlinear regression model and a mixed-effect model based on the power function. The nonlinear regression model yielded a similar exponent as the power function in the fluence compared with RG1.99/2. The mixed-effect model had a higher value of the exponent and showed superior goodness of fit compared with the nonlinear regression model. Compared with RG1.99/2 and RG1.99/3, the mixed-effect model provided a more accurate prediction of the TTS.

The Energy Efficient for Wireless Sensor Network Using The Base Station Location

  • Baral, Shiv Raj;Song, Young-Il;Jung, Kyedong;Lee, Jong-Yong
    • International Journal of Internet, Broadcasting and Communication
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    • v.7 no.1
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    • pp.23-29
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    • 2015
  • Energy constraints of wireless sensor networks are an important challenge. Data Transmission requires energy. Distance between origin and destination has an important role in energy consumption. In addition, the location of base station has a large impact on energy consumption and a specific method not proposed for it. In addition, a obtain model for location of base station proposed. Also a model for distributed clustering is presented by cluster heads. Eventually, a combination of discussed ideas is proposed to improve the energy consumption. The proposed ideas have been implemented over the LEACH-C protocol. Evaluation results show that the proposed methods have a better performance in energy consumption and lifetime of the network in comparison with similar methods.

A Study for Lifespan Prediction of Expansion by Temperature Status (온도상태에 따른 신축관 이음의 수명예측에 관한 연구)

  • Oh, Jung-Soo;Lee, Bong-Soo
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.19 no.10
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    • pp.424-429
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    • 2018
  • In this study, an expansion joint that is susceptible to waterhammer was tested for its vibration durability. The operation data for the hydraulic actuator was the expansion length of the expansion joint when the waterhammer occurred. In the case of the vibration durability test, the internal temperature status of the expansion joint was assumed to be a stress factor and a lifespan prediction model was assumed to follow the Arrhenius model. A test was carried out by increasing the internal temperature status at $30^{\circ}C$, $50^{\circ}C$, and $65^{\circ}C$. By a linear transformation of the lifespan data for each temperature, a constant value and activation energy coefficient was induced for the Arrhenius equation and verified by comparing the value of a lifetime prediction model with the experimental value at $85^{\circ}C$. The failure modes of the ongoing or finished test were leakage, bellows separation, and internal deformation. In the future, a composite lifespan prediction model, including two more stress factors, will be developed.

Service life prediction of chloride-corrosive concrete under fatigue load

  • Yang, Tao;Guan, Bowen;Liu, Guoqiang;Li, Jing;Pan, Yuanyuan;Jia, Yanshun;Zhao, Yongli
    • Advances in concrete construction
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    • v.8 no.1
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    • pp.55-64
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    • 2019
  • Chloride corrosion has become the main factor of reducing the service life of reinforced concrete structures. The object of this paper is to propose a theoretical model that predicts the service life of chloride-corrosive concrete under fatigue load. In the process of modeling, the concrete is divided into two parts, microcrack and matrix. Taking the variation of mcirocrack area caused by fatigue load into account, an equation of chloride diffusion coefficient under fatigue load is established, and then the predictive model is developed based on Fick's second law. This model has an analytic solution and is reasonable in comparison to previous studies. Finally, some factors (chloride diffusion coefficient, surface chloride concentration and fatigue parameter) are analyzed to further investigate this model. The results indicate: the time to pit-to-crack transition and time to crack growth should not be neglected when predicting service life of concrete in strong corrosive condition; the type of fatigue loads also has a great impact on lifetime of concrete. In generally, this model is convenient to predict service life of chloride-corrosive concrete with different water to cement ratio, under different corrosive condition and under different types of fatigue load.

Fatigue life prediction for radial truck tires using a global-local finite element method

  • Jeong, Kyoung Moon;Beom, Hyeon Gyu;Kim, Kee-Woon;Cho, Jin-Rae
    • Interaction and multiscale mechanics
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    • v.4 no.1
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    • pp.35-47
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    • 2011
  • A global-local finite element modeling technique is employed in this paper to predict the fatigue life of radial truck tires. This paper assumes that a flaw exists inside the tire, in the local model. The local model uses an FEM fracture analysis in conjunction with a global-local technique in ABAQUS. A 3D finite element local model calculates the energy release rate at the belt edge. Using the analysis of the local model, a study of the energy release rate is performed in the crack region and used to determine the crack growth rate analysis. The result considers how different driving conditions contribute to the detrimental effects of belt separation in truck tire failure. The calculation of the total mileage on four sizes of radial truck tires has performed on the belt edge separation. The effect of the change of belt width design on the fatigue lifetime of tire belt separation is discussed.

Prediction of Inhalation Exposure to Benzene by Activity Stage Using a Caltox Model at the Daesan Petrochemical Complex in South Korea (CalTOX 모델을 이용한 대산 석유화학단지의 활동단계에 따른 벤젠 흡입 노출평가)

  • Lee, Jinheon;Lee, Minwoo;Park, Changyong;Park, Sanghyun;Song, Youngho;Kim, Ok;Shin, Jihun
    • Journal of Environmental Health Sciences
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    • v.48 no.3
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    • pp.151-158
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    • 2022
  • Background: Chemical emissions in the environment have rapidly increased with the accelerated industrialization taking place in recent decades. Residents of industrial complexes are concerned about the health risks posed by chemical exposure. Objectives: This study was performed to suggest modeling methods that take into account multimedia and multi-pathways in human exposure and risk assessment. Methods: The concentration of benzene emitted at industrial complexes in Daesan, South Korea and the exposure of local residents was estimated using the Caltox model. The amount of human exposure based on inhalation rate was stochastically predicted for various activity stages such as resting, normal walking, and fast walking. Results: The coefficient of determination (R2) for the CalTOX model efficiency was 0.9676 and the root-mean-square error (RMSE) was 0.0035, indicating good agreement between predictions and measurements. However, the efficiency index (EI) appeared to be a negative value at -1094.4997. This can be explained as the atmospheric concentration being calculated only from the emissions from industrial facilities in the study area. In the human exposure assessment, the higher the inhalation rate percentile value, the higher the inhalation rate and lifetime average daily dose (LADD) at each activity step. Conclusions: Prediction using the Caltox model might be appropriate for comparing with actual measurements. The LADD of females was higher ratio with an increase in inhalation rate than those of males. This finding would imply that females may be more susceptible to benzene as their inhalation rate increases.

A Methodology of Customer Churn Prediction based on Two-Dimensional Loyalty Segmentation (이차원 고객충성도 세그먼트 기반의 고객이탈예측 방법론)

  • Kim, Hyung Su;Hong, Seung Woo
    • Journal of Intelligence and Information Systems
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    • v.26 no.4
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    • pp.111-126
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    • 2020
  • Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.

Prescreening of Environmental Conditions for Prediction of Severe Operation Condition of Offshore Structures

  • Lim, Dong-Hyun;Kim, Yonghwan;Kim, Taeyoung
    • Journal of Advanced Research in Ocean Engineering
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    • v.1 no.4
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    • pp.252-267
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    • 2015
  • Offshore structures might encounter several environmental and operating conditions during their lifetime of several decades. In order to predict the dynamic behavior of offshore structures, several simulation cases should be considered to deal with all the combinations of ocean environments and operating conditions. Because a sophisticated time-domain coupled dynamic analysis requires an extremely large amount of computational time to handle all the possible cases, an efficient preliminary process to prescreen the probability of severe environmental conditions can be helpful in downsizing the number of simulation cases and computational effort. In this study, a prescreening procedure to reduce the number of environmental conditions for dynamic analyses of offshore structures is proposed. For the efficiency of the procedure, frequency-domain theories were adopted to estimate the platform offset, using quasi-static analyses in line tension prediction. The results were validated by comparing with those of dynamic analysis coupled between platform and mooring lines, and reasonable agreement was observed. In addition, the characteristics of environmental conditions classified to be severe to the system were investigated through the application of the developed prescreening scheme to several actual environmental conditions.

Reliability Assessment and Accelerated Life Prediction of Gas Welded Joint in the Rail Road Car Body (1. Plug and Ring Type) (철도차량 차체 가스용접 이음재의 가속수명예측과 신뢰도 평가)

  • Baek, Seung-Yeb
    • Journal of Welding and Joining
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    • v.28 no.1
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    • pp.77-85
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    • 2010
  • Stainless steel sheets are widely used as the structure material for the railroad cars and the commercial vehicles. These kinds structures used stainless steel sheets are commonly fabricated by using the gas welding. Gas welding is very important and useful technology in fabrication of a railroad car and vehicles structure.However fatigue strength of the gas welded joints is considerably lower than parent metal due to stress concentration at the weld, fatigue strength evaluation of gas welded joints are very important to evaluate the reliability and durability of railroad cars and to establish a criterion of long life fatigue design. In this paper, $({\Delta}{\sigma}_a)_R-N_f$ curve were obtained by fatigue tests. Using these results, the accelerated life test(ALT) was conducted. From the experimental results, an acceleration model was derived and acceleration factors are estimated. So it is intended to obtain the useful information for the fatigue lifetime of plug and ring gas welded joints and data analysis by statistic reliability method, to save time and cost, and to develop optimum accelerated life prediction plans.