Proceedings of the Annual Conference of the PHM Society, Vol. 11 No. 1, September 2019, DOI: 10.36001/phmconf.2019.v11i1.914

A Framework to Interpret Deep Learning-Based Health Management System with Human Interactions

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.

This article is available online here.

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