Date of Award
January 2016
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Travis Desell
Second Advisor
James Higgins
Abstract
This thesis examines building viable Recurrent Neural Networks (RNN) using Long Short Term Memory (LSTM) neurons to predict aircraft engine vibrations. The different networks are trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, and this database contains multiple types of engines. Further, LSTM RNNs provide a “memory” of the contribution of previous time series data which can further improve predictions of future vibration values. LSTM RNNs were used over traditional RNNs, as those suffer from vanishing/exploding gradients when trained with back propagation. The study managed to predict vibration values for 1, 5, 10, and 20 seconds in the future, with 2.84% 3.3%, 5.51% and 10.19% mean absolute error, respectively. These neural networks provide a promising means for the future development of warning systems so that suitable actions can be taken before the occurrence of excess vibration to avoid unfavorable situations during flight.
Recommended Citation
Elsaid, Abdelrahman, "Using Long-Short-Term-Memory Recurrent Neural Networks To Predict Aviation Engine Vibrations" (2016). Theses and Dissertations. 2012.
https://commons.und.edu/theses/2012