Non-stationary vibration fatigue life prediction of automotive components based on long short-term memory network

Abstract

Automotive components are prone to fatigue failure as a result of the long-term effects of vibration loads. Due to the significant non-stationarity of irregular excitations from various road surfaces, the classical frequency-domain method struggles to accurately estimate the fatigue life of automotive components. Based on long short-term memory (LSTM) networks, an efficient time-domain method for non-stationary vibration fatigue life prediction is proposed. Firstly, the data augmentation method for simulating long-time non-stationary loads is studied. Short-time histories are transformed into time–frequency spectrograms, and then the time–frequency spectrums are warped and masked to reconstruct the long-time non-stationary loads. Furthermore, employing only short-time loads and responses as training samples, the LSTM network is trained to construct a surrogate model for calculating structural stress time histories. Finally, the responses of varying-length long-time loads are calculated, and fatigue life is predicted by the combination of rainflow counting and Miner rule. Additionally, the representative response durations required for the fatigue analysis are estimated. Numerical simulation of control arms shows that the fatigue life prediction results using the LSTM surrogate model are within 1.9% difference compared to transient dynamics analysis results based on finite element method, and the calculation efficiency is improved by orders of magnitude.