Growing interest in using Electromechanical Actuators (EMAs) to replace current hydraulic actuation methods on aircraft control surfaces has driven significant research in the area of prognostics and health management. Non- stationary speeds and loads in the course of controlling an aircraft surface make fault identification in EMAs difficult. This work presents a time- frequency analysis of EMA thrust bearing vibration signals using wavelet transforms. A relatively small EMA system is designed and built to allow for simple, quick, and repeatable component replacement. A simulated signal is developed to test four potential faults in the system. Classification is performed using an artificial neural network (ANN), which yields over 99% accuracy. Indentation faults from moderate and heavy loads are seeded in thrust bearings, which are then tested to generate data. The ANN achieves 95% classification accuracy in a two class scenario using healthy and moderately indented bearings. A three class test is executed using thrust bearings at each level of damage to perform preliminary remaining useful life (RUL) testing, where an ANN is able to identify the fault severity with an accuracy of 88%.

Library of Congress Subject Headings

Actuators--Testing; Electromechanical devices--Testing; Neural networks (Computer science)

Publication Date


Document Type


Student Type


Degree Name

Mechanical Engineering (MS)

Department, Program, or Center

Mechanical Engineering (KGCOE)


Jason Kolodziej

Advisor/Committee Member

Agamemnon Crassidis

Advisor/Committee Member

Marca Lam


Physical copy available from RIT's Wallace Library at TJ223.A25 C73 2014


RIT – Main Campus

Plan Codes