Title page for ETD etd-01152008-125258


Document Type Master's Dissertation
Author Phillpotts, David Nicholas Charles
URN etd-01152008-125258
Document Title Nonlinear fault detection and diagnosis using Kernel based techniques applied to a pilot distillation colomn
Degree MEng (Control Engineering)
Department Chemical Engineering
Supervisor
Advisor Name Title
Prof P L de Vaal Supervisor
Keywords
  • statistical process control
  • fault diagnosis
  • fault detection
  • Kernel based methods
Date 2007-09-05
Availability unrestricted
Abstract

Fault detection and diagnosis is an important problem in process engineering. In this dissertation, use of multivariate techniques for fault detection and diagnosis is explored in the context of statistical process control. Principal component analysis and its extension, kernel principal component analysis, are proposed to extract features from process data. Kernel based methods have the ability to model nonlinear processes by forming higher dimensional representations of the data.

Discriminant methods can be used to extend on feature extraction methods by increasing the isolation between different faults. This is shown to aid fault diagnosis. Linear and kernel discriminant analysis are proposed as fault diagnosis methods.

Data from a pilot scale distillation column were used to explore the performance of the techniques. The models were trained with normal and faulty operating data. The models were tested with unseen and/or novel fault data. All the techniques demonstrated at least some fault detection and diagnosis ability. Linear PCA was particularly successful. This was mainly due to the ease of the training and the ability to relate the scores back to the input data. The attributes of these multivariate statistical techniques were compared to the goals of statistical process control and the desirable attributes of fault detection and diagnosis systems.

University of Pretoria

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