• Smart Media Journal
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• v.11 no.11
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• pp.63-74
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• 2022

Nowadays, lithium-ion battery has become more popular around the world. Knowing when batteries reach their end of life (EOL) is crucial. Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is needed for battery health management systems and to avoid unexpected accidents. It gives information about the battery status and when we should replace the battery. With the rapid growth of machine learning and deep learning, data-driven approaches are proposed to address this problem. Extracting aging information from battery charge/discharge records, including voltage, current, and temperature, can determine the battery state and predict battery RUL. In this work, we first outlined the charging and discharging processes of lithium-ion batteries. We then summarize the proposed techniques and achievements in all published data-driven RUL prediction studies. From that, we give a discussion about the accomplishments and remaining works with the corresponding challenges in order to provide a direction for further research in this area.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.18-30
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• 2022

Predicting remaining useful life (RUL) becomes significant to implement prognostics and health management of industrial systems. The relevant studies have contributed to creating RUL prediction models and validating their acceptable performance; however, they are confined to drive reasonable preventive maintenance strategies derived from and connected with such predictive models. This paper proposes a data-driven preventive maintenance method that predicts RUL of industrial systems and determines the optimal replacement time intervals to lead to cost minimization in preventive maintenance. The proposed method comprises: (1) generating RUL prediction models through learning historical process data by using machine learning techniques including random forest and extreme gradient boosting, and (2) applying the system failure time derived from the RUL prediction models to the Weibull distribution-based minimum-repair block replacement model for finding the cost-optimal block replacement time. The paper includes a case study to demonstrate the feasibility of the proposed method using an open dataset, wherein sensor data are generated and recorded from turbofan engine systems.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.8
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• pp.739-744
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• 2009

Rail-pad is an important and readily replaceable component of a railway track, as it is the elastic layer between the rail and the sleeper. Characteristics and useful lifetime prediction of rail-pad was very important in design procedure to assure the safety and reliability. In this paper, the degradation of rail pad properties as a function of their in-service life is studied with a view of developing a technique for predicting the optimum period of track maintenance with regard to pad replacement. In order to investigate the useful lifetime, the accelerate test were carried out. Accelerated test 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. By using the acceleration test, several useful lifetime prediction for rail-pads were proposed.

• Journal of the Korean Society of Safety
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• v.14 no.2
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• pp.11-18
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• 1999

Proper fatigue life prediction procedures are needed for mechanical structures which requires high durability and reliability. In this paper, a fatigue life prediction procedure has been developed for predicting fatigue life of moving structure under variable loadings. The developed procedure was efficiently applied for a fatigue life calculation of a container crane. Especially, the procedure is useful for safety assessment by computer simulation. A computer program was developed for fatigue life assessment by adopting the forementioned procedure.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.104-111
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• 2006

The non-linear properties of rubber material which are described as strain energy function are important parameter to design and evaluate of rubber spring. These are determined by material tests which are uni-axial tension and bi-axial tension. The computer simulation using the nonlinear element analysis program executed to predict and evaluate the load capacity and stiffness for chevron spring. In order to investigate the heat-aging effects on the rubber material properties, the acceleration test were carried out. 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. By using the compression set test, several useful life prediction for rubber material were proposed.

• Journal of the Korea Safety Management & Science
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• v.26 no.1
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• pp.39-44
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• 2024

In order to prevent accidents via defective ammunition, this paper analyzes recent research on ammunition life prediction methodology. This workanalyzes current shelf-life prediction approaches by comparing the pros and cons of physical modeling, accelerated testing, and statistical analysis-based prediction techniques. Physical modeling-based prediction demonstrates its usefulness in understanding the physical properties and interactions of ammunition. Accelerated testing-based prediction is useful in quickly verifying the reliability and safety of ammunition. Additionally, statistical analysis-based prediction is emphasized for its ability to make decisions based on data. This paper aims to contribute to the early detection of defective ammunition by analyzing ammunition life prediction methodology hereby reducing defective ammunition accidents. In order to prepare not only Korean domestic war situation but also the international affairs from Eastern Europe and Mid East countries, it is very important to enhance the stability of organizations using ammunition and reduce costs of potential accidents.

• 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 KSR Conference
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• 2009.05a
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• pp.1010-1015
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• 2009

Rail-pad is an important and readily replaceable component of a railway track, as it is the elastic layer between the rail and the sleeper. Characteristics and useful lifetime prediction of rail-pad was very important in design procedure to assure the safety and reliability. In order to investigate the useful lifetime, the accelerate test were carried out. Accelerated test 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. By using the acceleration test, several useful lifetime prediction for rail-pads were proposed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3242-3265
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• 2023

Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.

• Transactions of the Korean Society of Mechanical Engineers
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• v.15 no.2
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• pp.596-603
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• 1991

Fatigue life prediction of pressure vessel is studied analytically using cumulative damage models and linear elastic fracture mechanics method. The stresses are analyzed by finite element method. During operation, the maximum stress occurs at the outside of neck region while fatigue analysis indicates that the bottom of nozzle part has the shortest fatigue life. Previously proposed fatigue life prediction equation and cumulative damage model are modified successfully by introducing reference fatigue modulus. It is found that the modified life prediction equation and damage model are useful for lower stress level application.