Machine learning can be enhanced through the integration of external knowledge. This method, called knowledge informed machine learning, is also applicable within the field of Prognostics and Health Management (PHM). In this paper, the various methods of knowledge informed machine learning, from a PHM context, are reviewed with the goal of helping the reader understand the domain. In addition, a knowledge informed machine learning technique is demonstrated, using the common IMS and PRONOSTIA bearing data sets, for remaining useful life (RUL) prediction. Specifically, knowledge is garnered from the field of reliability engineering which is represented through the Weibull distribution. The knowledge is then integrated into a neural network through a novel Weibull-based loss function. A thorough statistical analysis of the Weibull-based loss function is conducted, demonstrating the effectiveness of the method on the PRONOSTIA data set. However, the Weibull-based loss function is less effective on the IMS data set. The results, shortcomings, and benefits of the approach are discussed in length. Finally, all the code is publicly available for the benefit of other researchers.

本文通过回顾Prognostics and Health Management领域内知识增强机器学习的各种方法，并使用常见的IMS和PRONOSTIA轴承数据集，提出了一种基于Weibull分布的知识增强机器学习技术，并通过Weibull分布的损失函数将知识集成到神经网络中，对Weibull分布的损失函数进行了全面的统计分析，论证了该方法在PRONOSTIA数据集上的有效性。最后，本文公开了所有代码以便其他研究者使用。

利用基于威布尔损失函数的知识启发式机器学习