Deep Learning Inspired Prognostics Scheme for Applications Generating Big Data


In this paper, the relevance of deep neural network (DNN) is studied in big data scenarios, specifically for prognostics applications. It is observed that fault predictions can be performed more efficiently when DNN is used with a pre-processing step. A novel hierarchical dimension reduction (HDR) approach is therefore proposed as a pre-processing step to DNN. This two-step approach is shown to be effective in extracting value from complex and uncertain big data. It is shown that use of HDR prior to DNN improves convergence and allows for the possibility of reduction in model size without any drop in accuracy. A comprehensive methodology is developed to facilitate prognostics using DNN. Simulation results are included to demonstrate the overall methodology using big data-sets.

Meeting Name

2017 International Joint Conference on Neural Networks, IJCNN (2017: May 14-19, Anchorage, AK)


Electrical and Computer Engineering

Second Department

Mathematics and Statistics

Research Center/Lab(s)

Intelligent Systems Center

Second Research Center/Lab

Center for High Performance Computing Research


This research was supported in part by NSF I/UCRC awards IIP 1134721 and CMMI #1547042.

Keywords and Phrases

Data mining; Deep learning; Deep neural networks; Systems engineering; Dimension reduction; Fault prediction; Model size; Pre-processing step; Two-step approach; Big data

International Standard Book Number (ISBN)


International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 May 2017