Knowledge Informed Machine Learning using a Weibull-based Loss Function
This is an incremental improvement for researchers in Prognostics and Health Management, focusing on domain-specific knowledge integration.
The paper tackles the problem of remaining useful life prediction in Prognostics and Health Management by integrating reliability engineering knowledge via a novel Weibull-based loss function in neural networks, demonstrating effectiveness on the PRONOSTIA dataset but less so on the IMS dataset.
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.