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.

中文翻译：

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基于长短期记忆网络的汽车零部件非平稳振动疲劳寿命预测


由于振动载荷的长期影响，汽车部件很容易出现疲劳失效。由于各种路面的不规则激励具有显着的非平稳性，经典的频域方法难以准确估计汽车零部件的疲劳寿命。基于长短期记忆（LSTM）网络，提出了一种有效的时域非平稳振动疲劳寿命预测方法。首先，研究了模拟长时间非平稳载荷的数据增强方法。将短时历史转换为时频谱图，然后对时频谱进行扭曲和屏蔽以重建长时间非平稳载荷。此外，仅采用短时载荷和响应作为训练样本，训练 LSTM 网络以构建用于计算结构应力时程的替代模型。最后，计算不同长度长时间载荷的响应，并结合雨流计数和Miner规则预测疲劳寿命。此外，还估计了疲劳分析所需的代表性响应持续时间。控制臂数值仿真表明，采用LSTM代理模型的疲劳寿命预测结果与基于有限元方法的瞬态动力学分析结果相差在1.9%以内，计算效率提高了几个数量级。