• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.4
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• pp.355-362
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• 2012

Remaining useful life(RUL) prediction of a system is important in the prognostics field since it is directly linked with safety and maintenance scheduling. In the physics-based prognostics, accurately estimated model parameters can predict the remaining useful life exactly. It, however, is not a simple task to estimate the model parameters because most real system have multivariate model parameters, also they are correlated each other. This paper presents representative methods to estimate model parameters in the physics-based prognostics and discusses the difference between three methods; the particle filter method(PF), the overall Bayesian method(OBM), and the sequential Bayesian method(SBM). The three methods are based on the same theoretical background, the Bayesian estimation technique, but the methods are distinguished from each other in the sampling methods or uncertainty analysis process. Therefore, a simple physical model as an easy task and the Paris model for crack growth problem are used to discuss the difference between the three methods, and the performance of each method evaluated by using established prognostics metrics is compared.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.7
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• pp.799-804
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• 2011

A thermal cycling test was conducted on a chip resistor solder joint with real-time failure monitoring. In order to study the failure mechanism of the chip resistor solder joint during the test, the resistance between both ends of the resistor was monitored until the occurrence of failure. It was observed that the monitored resistance first fluctuated linearly according to the temperature change. The initial variation in the resistance occurred at the time during the cycle when there was a decrease in temperature. A more significant change in the resistance followed after a certain number of cycles, during the time when there was an increase in the temperature. In order to explain the failure patterns of the solder joint, a mechanism for the solder failure was suggested, and its validity was proved through FE simulations. Based on the explained failure mechanism, it was shown that prognostics for the solder failure can be implemented by monitoring the resistance change in a thermal cycle condition.

• Journal of Aerospace System Engineering
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• v.13 no.6
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• pp.17-25
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• 2019

PHM (Prognostics and Health Management) of aircraft engines is applied to predict the remaining useful life before failure or the lifetime limit. There are two methods to establish a predictive model for this: The physics-based method and the data-driven method. The physics-based method is more accurate and requires less data, but its application is limited because there are few models available. In this study, the data-driven method is applied, in which a multi-layer perceptron based neural network algorithms is applied for the life prediction. The neural network is trained using the data sets virtually made by the C-MAPSS code developed by NASA. After training the model, it is applied to the test data sets, in which the confidence interval of the remaining useful life is predicted and validated by the actual value. The performance of proposed method is compared with previous studies, and the favorable accuracy is found.