Data-driven health prognostic is attracting more and more attention to machinery prognostic and health management. It enables machinery to realize predictive maintenance and rarely depends on prior knowledge of degradation mechanisms. However, cross-domain health prognostic may lack enough measured data as supports, and this bottleneck is particularly prominent in high-end manufacturing. As such, this paper aims to improve prediction performances under limited data coupled with variable working conditions. Meta learning is introduced into this field for the first time, and meta deep learning (MDL) based health prognostic methodologies toward few-shot prognostics are further proposed. To be specific, time–frequency images and time-series data are first picked up for abstracting domain-invariant degradation indicators based on the integration of covariance matrices and maximum mean discrepancy. Then the subtask and cross-subtask level gradient based optimization architecture is conducted to abstract more general degradation knowledge for prognostics models’ adaptation. Based on the architecture, two variants termed as meta convolutional neural network (meta CNN) and meta gated recurrent unit (meta GRU) are proposed to accomplish few-shot prognostics with different forms of degradation indicators. Thirdly, three cases of run-to-failed machinery experiments are employed for a large number of verifications to avoid unexpected results. Finally, appealing predictions compared with existing methods demonstrate the superiority of our proposed MDL health prognostics.

数据驱动的健康预后越来越引起人们对机械预后和健康管理的关注。它使机器能够实现预测性维护，并且很少依赖于退化机制的先验知识。但是，跨域健康预后可能缺乏足够的测量数据作为支持，而这一瓶颈在高端制造业中尤为突出。因此，本文旨在提高有限数据和可变工作条件下的预测性能。元学习首次被引入该领域，并进一步提出了基于元深度学习（MDL）的针对少发事件预测的健康预测方法。再具体一点，首先提取时频图像和时间序列数据，以基于协方差矩阵和最大均值差异的积分来提取领域不变的退化指标。然后进行基于子任务和跨子任务级别梯度的优化架构，以提取更一般的降级知识以用于预测模型的适应。在该架构的基础上，提出了两种称为元卷积神经网络（meta卷积神经网络）和元门控循环单元（meta GRU）的变体，以完成具有不同形式的退化指标的一次性预测。第三，为了避免意外的结果，使用了三项运行失败的机械实验案例进行了大量的验证。最后，