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

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Published Sep 22, 2019
Michael H. Azarian Namkyoung Lee Michael G. Pecht

Abstract

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.

How to Cite

Azarian, M. H., Lee, N., & Pecht, M. G. (2019). A Framework to Interpret Deep Learning-Based Health Management System with Human Interactions. Annual Conference of the PHM Society, 11(1). https://doi.org/10.36001/phmconf.2019.v11i1.914
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Keywords

Doctoral Symposium

Section
Doctoral Symposium Summaries