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Application of Machine Learning to Rotorcraft Health MonitoringMachine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.
Document ID
20170001402
Acquisition Source
Glenn Research Center
Document Type
Technical Memorandum (TM)
Authors
Cody, Tyler
(NASA Glenn Research Center Cleveland, OH, United States)
Dempsey, Paula J.
(NASA Glenn Research Center Cleveland, OH United States)
Date Acquired
February 8, 2017
Publication Date
January 1, 2017
Subject Category
Mathematical And Computer Sciences (General)
Report/Patent Number
E-19307
GRC-E-DAA-TN35995
NASA/TM-2017-219408
Funding Number(s)
WBS: WBS 664817.02.03.02.03.02
CONTRACT_GRANT: NNC16QA09D
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
Keywords
machine learning
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