Title

LSTM-based ensemble learning for time-dependent reliability analysis

Document Type

Conference Proceeding

Publication Date

1-1-2020

Department

Department of Mechanical Engineering-Engineering Mechanics

Abstract

This paper presents a long short-term memory (LSTM)- based ensemble learning framework for time-dependent reliability analysis. To deal with the time-dependent uncertainties, a LSTM network is first adopted to capture the system dynamics. As a result, time-dependent system responses for random realizations of stochastic processes can be accurately predicted by the LSTM. With realizations of the random variables and stochastic processes, multiple LSTMs are trained for generating a set of augmented data. Then a deep feedforward neural network (DFN) is employed to ensemble the knowledge extracted from LSTMs and generate a deep surrogate for the original time-dependent system responses. To improve the performance of DFN in terms of accuracy, the Gaussian process modeling technique is utilized for architecture design, where the number of neurons in the hidden layer is determined by minimizing the validation loss. With the DFN, the timedependent system reliability can be directly approximated by using the Monte Carlo simulation. Two case studies are introduced to demonstrate the efficiency and accuracy of the proposed approach.

Publisher's Statement

Copyright © 2020 ASME. Publisher’s version of record: https://doi.org/10.1115/DETC2020-22006

Publication Title

Proceedings of the ASME Design Engineering Technical Conference

ISBN

9780791884010

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