  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.
