Masters Theses

Abstract

"In this thesis, the diagnosis and prognosis of single and simultaneous multiple incipient faults in practical industrial systems is demonstrated in two papers. For the purpose of fault detection and failure prediction, a model based strategy is adopted, which involves using an online adaptive estimator. The online estimator estimates the system dynamics. Residual is generated based on the physical system model and the estimated system dynamics. This residual is used to detect the fault. Once the fault is detected, a tunable nonlinear neural network online approximator in discrete-time (OLAD) along with a robust adaptive term is initiated. The OLAD utilizes the parameter update law to learn the unknown fault dynamics of the encountered fault. The robust adaptive term guarantees the asymptotic convergence of the residual. Once the fault is detected, fault isolation and time-to-failure (ITF) prediction are performed by another online approximator.

In the first paper, a vapor compression system with a scroll compressor is taken and analyzed for mechanical faults which are refrigeration cycle faults like refrigerant leakage and heat exchanger fouling. In the second paper, a 3 phase squirrel cage Induction motor with a variable load like a vapor compressor is considered to study for electrical faults like the insulation degradation and rotor bar breakage faults. Thus the scheme can be employed to a wide variety of industrial systems and can be used to detect and predict different types of faults"--Abstract, page iv.

Advisor(s)

Zawodniok, Maciej Jan, 1975-

Committee Member(s)

Sarangapani, Jagannathan, 1965-
Acar, Levent

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Electrical Engineering

Publisher

Missouri University of Science and Technology

Publication Date

2011

Journal article titles appearing in thesis/dissertation

  • Model based diagnostics and prognostics of a scroll compressor used in vapor compression system applications
  • Model based diagnostics and prognostics of 3 phase induction motor for vapor compressor applications

Pagination

xii, 70 pages

Note about bibliography

Includes bibliographical references.

Rights

© 2011 Raja Sekhar Kraleti, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Subject Headings

Electric fault location -- Detection
Electric machinery -- Testing
Electric motors, Induction -- Testing

Thesis Number

T 10558

Print OCLC #

908208811

Electronic OCLC #

908260757

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