Title page for ETD etd-10082006-190542


Type of Document Dissertation
Author Luo, Min
URN etd-10082006-190542
Title Data-Driven Fault Detection Using Trending Analysis
Degree Doctor of Philosophy (Ph.D.)
Department Electrical & Computer Engineering
Advisory Committee
Advisor Name Title
Jorge Aravena Committee Chair
Bahadir Gunturk Committee Member
JianHua Chen Committee Member
Kemin Zhou Committee Member
Mort Naraghi-Pour Committee Member
Greg Stacy Dean's Representative
Keywords
  • fault detection
  • fault isolation
  • convex hull
  • subsequence based support vector machine
Date of Defense 2006-08-25
Availability unrestricted
Abstract
The objective of this research is to develop data-driven fault detection methods which do not rely on mathematical models yet are capable of detecting process malfunctions. Instead of using mathematical models for comparing performances, the methods developed rely on extensive collection of data to establish classification schemes that detect faults in new data. The research develops two different trending approaches. One uses the normal data to define a one-class classifier. The second approach uses a data mining technique, e.g. support vector machine (SVM) to define multi class classifiers. Each classifier is trained on a set of example objects.

The one-class classification assumes that only information of one of the classes, namely the normal class, is available. The boundary between the two classes, normal and faulty, is estimated from data of the normal class only. The research assumes that the convex hull of the normal data can be used to define a boundary separating normal and faulty data.

The multi class classifier is implemented through several binary classifiers. It is assumed that data from two classes are available and the decision boundary is supported from both sides by example objects. In order to detect significant trends in the data the research implements a non-uniform quantization technique, based on Lloyd’s algorithm and defines a special subsequence-based kernel. The effect of the subsequence length is examined through computer simulations and theoretical analysis.

The test bed used to collect data and implement the fault detection is a six degrees of freedom, rigid body model of a B747 100/200 and only faults in the actuators are considered. In order to thoroughly test the efficiency of the approach, the test use only sensor data that does not include manipulated variables. Even with this handicap the approach is effective with the average of 79.5% correct detection and 16.7% missed alarm and 3.9% false alarms for six different faults.

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