Rolling bearing is a vital part of the machinery, whose remaining useful life (RUL) estimation plays a critical role in ensuring the safety and maintenance decision-making. However, in most industrial applications, it is difficult to obtain run-to-failure data under complex operating conditions, which is inefficient for deep learning approaches. To solve the above problem, a new approach using transfer depth-wise separable convolution recurrent network (TDSCRN) for RUL estimation of bearing is presented. A novel prediction model so-called depth-wise separable convolution recurrent network (DSCRN) is designed and trained by the source-domain dataset. The parameters and model of DSCRN are transferred to the target-domain, and then TDSCRN is obtained for RUL estimation task. Two public run-to-failure datasets are used to validate the performance of the presented method. The results indicate that this framework can improve estimation accuracy and robustness in complex operating conditions.

滚动轴承是机械的重要组成部分，其剩余使用寿命（RUL）估算对于确保安全和维护决策至关重要。但是，在大多数工业应用中，很难在复杂的操作条件下获取运行失败数据，这对于深度学习方法而言效率不高。为了解决上述问题，提出了一种使用传递深度方向可分离卷积递归网络（TDSCRN）进行轴承RUL估计的新方法。源域数据集设计并训练了一种新颖的预测模型，即所谓的深度可分离卷积递归网络（DSCRN）。将DSCRN的参数和模型传递到目标域，然后获得TDSCRN用于RUL估计任务。使用两个公共运行失败数据集来验证所提出方法的性能。结果表明，该框架可以提高复杂操作条件下的估计准确性和鲁棒性。