Namkyoung Lee, 1, Michael H. Azarian, 1, and Michael G. Pecht 1
1 Center for Advanced Life Cycle Engineering, University of Maryland at College Park, College Park, MD 20742, USA
Deep learning has shown good performance in detecting a product’s faults and estimating the remaining useful life of a product. However, it is hard to interpret deep learning-based health management systems because deep learning is often regarded as a black box. In order to make a maintenance decision based on the result of the management system, humans need to know how it gave the outcome. This study aims to develop a framework that utilizes human interactions during system development to understand the internal process of deep learning. The study will demonstrate the framework on bearing datasets.