Multibranch Horizontal Augmentation Network for Continuous Remaining Useful Life Prediction
针对连续剩余使用寿命预测中任务差异大、现有方法信息捕捉能力有限的问题，提出多分支水平增强网络，通过分层自注意力机制和时频融合时间卷积网络提取退化特征，并利用记忆权重约束实现连续学习，在轴承和齿轮数据集上平均准确率达93%。
Aiming at the large differences between tasks in continuous remaining useful life (RUL) prediction and the limited information capturing capability of the existing continuous learning (CL) methods, this article develops a novel multibranch horizontal augmentation network (MBHAN). First, a hierarchical self-attention (HSA) mechanism is proposed to capture the local degradation features and dependencies at different scales and enhance the representation capacity of RUL prediction model. Based on HSA and temporal convolutional network (TCN), a time-frequency fusion TCN (TFFTCN) is designed to mine the hidden degradation information from the time-domain and frequency-domain data. Then, a memory weight constraint (MWC) regularization term is built to control the update of important parameters for pervious tasks during the learning of new task. A horizontal network augmentation rule based on the task similarity and MWC is proposed, including the augmentation of a task branch network for small task difference and the augmentation of a feature extraction backbone network for large task difference. On this basis, the MBHAN is proposed to continuously predict RUL of machinery. Finally, the experimental results on the life-cycle bearing and gear datasets demonstrate that TFFTCN achieve an average accuracy of 93% across both datasets, surpassing the existing prediction methods.