An efficient method for time-dependent reliability prediction using domain adaptation
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RESEARCH PAPER
An efficient method for time-dependent reliability prediction using domain adaptation Tayyab Zafar 1,2,3 & Zhonglai Wang 1,2 Received: 5 February 2020 / Revised: 11 May 2020 / Accepted: 28 July 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Due to dynamic uncertainties presence in service and performance conditions, time-dependent reliability prediction of a component or structure is a challenging problem. In this research, a transfer learning-based technique is proposed to predict the reliability in the future. The complete time interval is divided into two sub-intervals namely, present interval and future interval. It is assumed that the performance function information is available for the present interval only. Transfer learning, specifically domain adaptation is used to transform the stochastic processes to be represented in a way that their sample spaces in different time durations are made closer while maintaining some of their statistical properties such as variance. In order to transform the stochastic processes, correlated samples of stochastic processes are generated using a space-filling sampling technique for the complete time interval. An adaptive Kriging surrogate model is then built using the performance information available for the present interval only using transformed stochastic process samples. The built Kriging model is employed to estimate and predict the reliability for present and future intervals without retraining it using future data. Results show that the proposed method can predict the failure probability in present and future intervals accurately with significant efficiency improvement. Keywords Time-dependent reliability prediction . Surrogate model . Kriging . Transfer learning . Domain adaptation
1 Introduction
Highlights of the paper • A transformation matrix is built to transform the stochastic process samples in the present and the future interval using domain adaptation. • A domain adaptation based Kriging surrogate model is used to predict the time-dependent reliability for the future interval. • A time-dependent reliability prediction method is proposed with improved efficiency and ensured accuracy. • The proposed method can predict the time-dependent reliability for the performance function involving stationary as well as non-stationary stochastic processes. Responsible Editor Shapour Azarm Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00158-020-02707-z) contains supplementary material, which is available to authorized users. * Zhonglai Wang [email protected] 1
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of China
A component or structure may undergo a catastrophic failure during a lifetime due to the existence of uncertainties. These uncertainties may originate from dynamic loading conditions, strength degradation with time, extreme operating conditions, and environmental condi
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