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Nonlinear filtering techniques for failure detection in dynamic systems

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Date Issued:
1989
Summary:
Developing on-line methods for detecting and locating process malfunctions is an important goal towards full automation of systems. Model-based methods, which check the consistency of the observations using the known functional relationships among the process variables seem to have the potential of early detection of slowly developing failures in complex systems. This dissertation deals with nonlinear filtering as a method for failure detection in dynamic processes. The failure detection problem is presented here as an Identification Problem, where no jumps between the possible operating modes are assumed, i.e. there is only uncertainty with regard to which is the present mode. The identification filter consists of parallel Kalman filters, each tuned to a different system mode of operation, whose estimates parametrize the conditional probability equations. An extension of the filter is derived for the case of different measurement noise coefficients in different modes for the continuous-discrete case. In the continuous-time case with different measurement noise intensities the likelihood function is shown to be ill-defined as the induced measures become singular. The average performance of the continuous-descrete as well as the continuous-time identification filters are studied. It is shown that on the average and after a sufficiently long time a correct decision is expected for any decision threshold level. An alternative identification filter structure is derived using the maximum-likelihood estimation philosophy. The filter reduces to parallel Kalman filters, feeding the state estimates to a maximum likelihood generator which then chooses a set of indicator functions to maximize the total likelihood. Some aspects of interpreting a sequence of decisions, choosing a decision threshold and reinitializing the filter are discussed qualitatively using simulation examples. Furthermore, a new approach based on scaling of the likelihood functions is presented. Scaling is shown to be equivalent to choosing a threshold level for the conditional probabilities.
Title: Nonlinear filtering techniques for failure detection in dynamic systems.
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Name(s): Ruokonen, Tuula.
Florida Atlantic University, Degree grantor
Roth, Zvi S., Thesis advisor
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 1989
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 181 p.
Language(s): English
Summary: Developing on-line methods for detecting and locating process malfunctions is an important goal towards full automation of systems. Model-based methods, which check the consistency of the observations using the known functional relationships among the process variables seem to have the potential of early detection of slowly developing failures in complex systems. This dissertation deals with nonlinear filtering as a method for failure detection in dynamic processes. The failure detection problem is presented here as an Identification Problem, where no jumps between the possible operating modes are assumed, i.e. there is only uncertainty with regard to which is the present mode. The identification filter consists of parallel Kalman filters, each tuned to a different system mode of operation, whose estimates parametrize the conditional probability equations. An extension of the filter is derived for the case of different measurement noise coefficients in different modes for the continuous-discrete case. In the continuous-time case with different measurement noise intensities the likelihood function is shown to be ill-defined as the induced measures become singular. The average performance of the continuous-descrete as well as the continuous-time identification filters are studied. It is shown that on the average and after a sufficiently long time a correct decision is expected for any decision threshold level. An alternative identification filter structure is derived using the maximum-likelihood estimation philosophy. The filter reduces to parallel Kalman filters, feeding the state estimates to a maximum likelihood generator which then chooses a set of indicator functions to maximize the total likelihood. Some aspects of interpreting a sequence of decisions, choosing a decision threshold and reinitializing the filter are discussed qualitatively using simulation examples. Furthermore, a new approach based on scaling of the likelihood functions is presented. Scaling is shown to be equivalent to choosing a threshold level for the conditional probabilities.
Identifier: 12238 (digitool), FADT12238 (IID), fau:9144 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (Ph.D.)--Florida Atlantic University, 1989.
Subject(s): Kalman filtering
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/12238
Sublocation: Digital Library
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.