Application of a Recurrent Neural Network and Simplified Semianalytical Method for Continuous Strain Histories Estimation

Abstract

The durability and reliability of structural components are usually assessed based on fatigue loading under operating conditions. To obtain accurate fatigue loading in the form of continuous strain histories, a novel approach is proposed based on the combination of a recurrent neural network and simplified semianalytical method. The recurrent neural network named nonlinear autoregressive model with exogenous inputs (NLARX) is applied to determine the relationship between external loads and corresponding fatigue loading. Owing to the generalization ability of NLARX, semianalytical method, which is used to obtain sample database for NLARX model training and testing, is implemented with simplified multibody model. Durability tests of a torsion beam rear suspension are introduced to demonstrate the effectiveness of the proposed approach. The experimental results show that our proposed approach is able to achieve better estimation results, when compared with the conventional semianalytical method.