论文标题
知识知情的机器学习使用基于威布尔的损失功能
Knowledge Informed Machine Learning using a Weibull-based Loss Function
论文作者
论文摘要
可以通过集成外部知识来增强机器学习。这种称为知识知情的机器学习的方法也适用于预后和健康管理（PHM）领域。在本文中，从PHM上下文中审查了各种知识知情的机器学习方法，目的是帮助读者了解域。此外，使用常见的IMS和Pronostia轴承数据集证明了知识知情的机器学习技术，以保持使用寿命（RUL）预测。具体而言，知识是从可靠性工程领域获得的，该工程通过Weibull分布表示。然后，通过基于Weibull的新型损失函数将知识集成到神经网络中。对基于Weibull的损失函数进行了详尽的统计分析，证明了该方法对原则数据集的有效性。但是，基于Weibull的损失函数在IMS数据集上的有效性较小。该方法的结果，缺点和益处将长时间讨论。最后，所有代码都是为了其他研究人员的利益而公开使用的。
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.
