Masters Theses

Date of Award

5-2018

Degree Type

Thesis

Degree Name

Master of Science

Major

Mechanical Engineering

Major Professor

Trevor M. Moeller

Committee Members

Milton W. Davis, James L. Simonton

Abstract

The Arnold Engineering Development Complex (AEDC) has identified a need to process data from oscillatory signals on a revolution basis, also known as order processing. Such oscillatory data is hereafter referred to as dynamic data. Order processing would serve to improve dynamic data accuracy as reported in the frequency and order domains, capture momentary integral responses, and facilitate organic comparisons between various types of oscillatory signals. Organizing data by revolutions would also be beneficial for time domain analysis.This paper explores the need for order processing, reviews similar methods employed in other data acquisition applications such as blade tip timing, and discusses options for making order processing possible for any oscillatory signal generated by rotating equipment. This paper primarily deals with turbine engine vibratory instrumentation and data acquisition. The content discussed herein may also be extended to other types of rotating equipment such as motors, compressors, and turbines. Order processing deals primarily with integral (synchronous) responses, which are forced responses as a function of rotational speed and natural frequencies. Non-synchronous responses, also known as non-integral (NIV) responses, are not considered.The focus of this paper lies in the research of conditioning time domain data sets of various sizes to be transformed to the frequency domain by means of FFTs with standard 2x sizes. This is to be accomplished while varying numbers of samples per revolution for a full range of rotational speeds. Succinctly stated, a comparison is made between standard FFT processing results and simplistic order processing methods. Since improved accuracy is one of the major drivers for developing this capability, the focal points are the acquisition, conditioning, and processing of virtual data sets that are of different sizes than specified FFT sizes. More specifically, the effects of decimation, zero padding, windowing functions, and other types of processing variables are evaluated. This research serves as a precursor for development of comprehensive order processing capabilities for turbine engines at AEDC.

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