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State-Augmented Mutating Particle Filtering for Fault Detection and Diagnosis

Brown, Cameron (2020) State-Augmented Mutating Particle Filtering for Fault Detection and Diagnosis. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

This research develops a model-based particle filter algorithm for quickly detecting sudden faults in dynamic systems. Faults are defined as the abnormal behavior or failure of the system components. This novel method avoids the numerical issues of some other model-based methods. It also allows the fault magnitudes to take on continuous values, instead of constraining them to discrete values.

The multiple-model particle filter (MMPF) and interacting multiple-model particle filter (IMMPF) techniques are tested on a nuclear reactor pressurizer system for the detection of loss-of-coolant accidents (LOCA). The drawbacks of these methods leads us to the develop the novel algorithm: the state-augmented mutating particle filter (SAMPF), which uses random walk techniques. The SAMPF detects sudden faults faster than conventional random walk techniques. Choosing the proper parameters for the algorithm is considered. The performance of the SAMPF is compared to that of the IMMPF for the pressurizer system. The SAMPF is superior to the IMMPF in fault diagnosis accuracy and consistency.

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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Brown, Cameroncjb95@pitt.educjb95
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairCole, Danieldgcole@pitt.edu
Committee MemberVipperman, Jeffreyjsv@pitt.edu
Committee MemberClark, Williamwclark@pitt.edu
Date: 28 January 2020
Date Type: Publication
Defense Date: 13 August 2019
Approval Date: 28 January 2020
Submission Date: 16 August 2019
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 77
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Mechanical Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: N/A
Date Deposited: 28 Jan 2020 17:44
Last Modified: 28 Jan 2020 17:44
URI: http://d-scholarship.pitt.edu/id/eprint/37385

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