Title page for ETD etd-06222000-11480046

Type of Document Dissertation
Author Mugtussids, Iossif B.
Author's Email Address imugtuss@vt.edu
URN etd-06222000-11480046
Title Flight Data Processing Techniques To identify Unusual Events
Degree PhD
Department Aerospace and Ocean Engineering
Advisory Committee
Advisor Name Title
Anderson, Mark R. Committee Chair
Cliff, Eugene M. Committee Member
Durham, Wayne C. Committee Member
Hall, Christopher D. Committee Member
Lutze, Frederick H. Jr. Committee Member
  • Pattern Recognition
  • Flight Data Recorders
  • Flight Data Analysis
  • Feature Generation
  • Clustering
  • Feature Selection
  • Classification
  • Bayes' Classifier
Date of Defense 2000-06-12
Availability unrestricted
Modern aircraft are capable of recording hundreds of parameters during

flight. This fact not only facilitates the investigation of an

accident or a serious incident, but also provides the opportunity to use

the recorded data to predict future aircraft behavior. It is believed

that, by analyzing the recorded data, one can identify precursors to

hazardous behavior and develop procedures to mitigate the problems

before they actually occur. Because of the enormous amount of data

collected during each flight, it becomes necessary to identify the

segments of data that contain useful information. The objective is to

distinguish between typical data points, that are present in the

majority of flights, and unusual data points that can be only found in

a few flights. The distinction between typical and unusual data points

is achieved by using classification procedures.

In this dissertation, the application of classification procedures to

flight data is investigated. It is proposed to use a Bayesian

classifier that tries to identify the flight from which a particular

data point came. If the flight from which the data point came

is identified with a high level of confidence, then the conclusion that

the data point is unusual within the investigated flights can be made.

The Bayesian classifier uses the overall and conditional probability

density functions together with a priori probabilities to make a

decision. Estimating probability density functions is a difficult task

in multiple dimensions. Because many of the recorded signals

(features) are redundant or highly correlated or are very similar in

every flight, feature selection techniques are applied to identify

those signals that contain the most discriminatory power. In the

limited amount of data available to this research, twenty five features were

identified as the set exhibiting the best discriminatory power.

Additionally, the number of signals is reduced by applying feature

generation techniques to similar signals.

To make the approach applicable in practice, when many flights are

considered, a very efficient and fast sequential data clustering

algorithm is proposed. The order in which the samples are presented to

the algorithm is fixed according to the probability density function

value. Accuracy and reduction level are controlled using two scalar

parameters: a distance threshold value and a maximum compactness


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