Remaining useful life (RUL) prediction is a challenging task for prognostics and health management (PHM). Due to the complexity physics involved for precisely modeling the machine degradation process, learning-based data-driven methods, which learn the degradation pattern solely from the historical data without referring to physical models, have become promising alternatives to model-based prognostic methods. In this paper, a new temporal convolutional neural network (TCN) with soft threshold and attention mechanism is proposed for machinery prognostics. Multi-channel sensor data are directly used as inputs to the prognostic network without feature extraction as a pre-processing step. A soft thresholding mechanism is embedded in the network, serving as a flexible activation function for certain layers to preserve useful features. The threshold value is adaptively learned by a subnetwork trained with the attention mechanism instead of assigning a deterministic value to the threshold. As a result, each feature map is assigned a customized threshold value such that the network training process can focus on features that are more critical to RUL prediction. To verify the generalization ability of the proposed method, three benchmark datasets related to rolling bearings and cutting tools are tested, and the performance of the developed method is compared with several state-of-the-art prognostic approaches. The results show that for all the three case studies, the developed method has produced accurate RUL prediction with good robustness and generalization ability.

剩余使用寿命 (RUL) 预测是预后和健康管理 (PHM) 的一项具有挑战性的任务。由于精确建模机器退化过程涉及复杂的物理学，基于学习的数据驱动方法仅从历史数据中学习退化模式而不参考物理模型，已成为基于模型的预测方法的有前途的替代方案。在本文中，提出了一种具有软阈值和注意机制的新时间卷积神经网络 (TCN) 用于机械预测。多通道传感器数据直接用作预测网络的输入，无需特征提取作为预处理步骤。网络中嵌入了软阈值机制，作为某些层的灵活激活函数，以保留有用的特征。阈值由使用注意力机制训练的子网络自适应学习，而不是为阈值分配确定性值。因此，每个特征图都被分配了一个定制的阈值，以便网络训练过程可以专注于对 RUL 预测更关键的特征。为了验证所提出方法的泛化能力，测试了与滚动轴承和刀具相关的三个基准数据集，并将所开发方法的性能与几种最先进的预测方法进行了比较。结果表明，对于所有三个案例研究，所开发的方法都产生了具有良好鲁棒性和泛化能力的准确 RUL 预测。