• Water Engineering Research
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• v.3 no.2
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• pp.113-122
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict flash floods. In this study, a Quantitative Flood Forecasting (QFF) model was developed by incorporating the evolving structure and frequency of intense weather systems and by using neural network approach. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as lifetime, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. All these processes stretched leadtime up to 18 hours. The QFF model will be applied to the mid-Atlantic region of United States in a forthcoming paper.

• Journal of Applied Reliability
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• v.13 no.1
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• pp.65-76
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• 2013

In order to assess the reliability of engine mount for a vehicles, life test model and procedure are developed. By using this method, failure mechanism and life distribution are analyzed. The main results are as follows; i) the main failure mechanism is degradation failure of engine mount rubber by fatigue failure at dynamic load. ii) temperature is a second factor to affect a failure. iii) the life distribution of engine mount module is fitted well to Weibull life distribution and the shape parameter is 18.4 and the accelerated life model of that is fitted well to Arrhenius model.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.923-931
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• 2008

Coupling rubber pad are important components in railway vehicles. It can be used for reduce shock, vibration and noise. Simple tension, equi-biaxial tension and pure shear test were performed to acquire the coefficient of rubber material which were Mooney-Rivlin and Ogden model. The finite element analysis was executed to evaluate the behavior of deformation and stress distribution by using the commercial finite element analysis code. Useful life evaluation are very important in design procedure to assure the safety and reliability of the rubber components. In this paper, useful life prediction of rubber pad for railway vehicle were experimentally investigated. Accelerated heat-aging test for rubber material were carried. Compression set results changes as the threshold are used for assessment of the useful life and time to threshold value were plotted against reciprocal of absolute temperature to give the Arrhenius plot.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.111-118
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• 2023

For the implementation of a smart factory, it is necessary to collect data by connecting various sensors and devices in the manufacturing environment and to diagnose or predict failures in production facilities through data analysis. In this paper, to predict the residual useful lifetime of milling insert used for machining products in CNC machine, weight k-NN algorithm, Decision Tree, SVR, XGBoost, Random forest, 1D-CNN, and frequency spectrum based on vibration signal are investigated. As the results of the paper, the frequency spectrum does not provide a reliable criterion for an accurate prediction of the residual useful lifetime of an insert. And the weighted k-nearest neighbor algorithm performed best with an MAE of 0.0013, MSE of 0.004, and RMSE of 0.0192. This is an error of 0.001 seconds of the remaining useful lifetime of the insert predicted by the weighted-nearest neighbor algorithm, and it is considered to be a level that can be applied to actual industrial sites.

• Journal of Applied Reliability
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• v.9 no.4
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• pp.291-302
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• 2009

Accelerated and field degradation tests are performed for reliability assessment of an anticorrosive paint for steel structures. Test data were analyzed to obtain the degradation model and the life time distributions of the paint. A power law degradation model and lognormal performance distribution were used to predict the lifetime of the anticorrosive paint and the method of finding an acceleration factor is provided.

• Structural Engineering and Mechanics
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• v.59 no.5
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• pp.885-899
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• 2016

The energy-based approach to predict the fatigue crack growth behavior under constant and variable amplitude loading (VAL) of the aluminum alloy 2024 T351 has been investigated and detailed analyses discussed. Firstly, the plastic strain energy was determined per cycle for different block load tests. The relationship between the crack advance and hysteretic energy dissipated per block can be represented by a power law. Then, an analytical model to estimate the lifetime for each spectrum is proposed. The results obtained are compared with the experimentally measured results and the models proposed by Klingbeil's model and Tracey's model. The evolution of the hysteretic energy dissipated per block is shown similar with that observed under constant amplitude loading.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.236-241
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• 2024

Due to the recent emphasis on carbon neutrality and environmental regulations, the global electric vehicle (EV) market is experiencing rapid growth. This surge has raised concerns about the recycling and disposal methods for EV batteries. Unlike traditional internal combustion engine vehicles, EVs require unique and safe methods for the recovery and disposal of their batteries. In this process, predicting the lifespan of the battery is essential. Impedance and State of Charge (SOC) analysis are commonly used methods for this purpose. However, predicting the lifespan of batteries with complex chemical characteristics through electrical measurements presents significant challenges. To enhance the accuracy and precision of existing measurement methods, this paper proposes using a Long Short-Term Memory (LSTM) model, a type of deep learning-based recurrent neural network, to diagnose battery performance. The goal is to achieve safe classification through this model. The designed structure was evaluated, yielding results with a Mean Absolute Error (MAE) of 0.8451, a Root Mean Square Error (RMSE) of 1.3448, and an accuracy of 0.984, demonstrating excellent performance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.142-148
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• 2015

Due to advancements in technology and manufacturing capability, it is not uncommon that life tests yield no or few failures at low stress levels. In these situations it is difficult to analyse lifetime data and make meaningful inferences about product or system reliability. For some products or systems whose performance characteristics degrade over time, a failure is said to have occurred when a performance characteristic crosses a critical threshold. The measurements of the degradation characteristic contain much useful and credible information about product or system reliability. Degradation measurements of the performance characteristics of an unfailed unit at different times can directly relate reliability measures to physical characteristics. Reliability prediction based on physical performance measures can be an efficient and alternative method to estimate for some highly reliable parts or systems. If the degradation process and the distance between the last measurement and a specified threshold can be established, the remaining useful life is predicted in advance. In turn, this prediction leads to just in time maintenance decision to protect systems. In this paper, we describe techniques for mapping product or system which has degrading performance parameter to the associated classical reliability measures in the performance domain. This paper described a general modeling and analysis procedure for reliability prediction based on one dominant degradation performance characteristic considering pseudo degradation performance life trend model. This pseudo degradation trend model is based on probability modeling of a failure mechanism degradation trend and comparison of a projected distribution to pre-defined critical soft failure point in time or cycle.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1355-1360
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• 2008

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 an 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 weldment, 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, ${\Delta}P-N_f$ curve were obtained by fatigue tests. Using these results, the accelerated life test (ALT) is conducted. From the experimental results, an acceleration model is 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.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.3
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• pp.1-8
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• 2011

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 an 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 weldment, 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, ${\Delta}-N_f$ curve were obtained by fatigue tests. Using these results, the accelerated life test (ALT) is conducted. From the experimental results, an acceleration model is 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 statistical reliability method, to save time and cost, and to develop optimum accelerated life prediction plans.