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FAULT DIAGNOSIS WITH RECONSTRUCTION-BASED
CONTRIBUTIONS FOR STATISTICAL PROCESS MONITORING
by
Carlos Felipe Alcala Perez
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Ful llment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(CHEMICAL ENGINEERING)
August òýÔÔ
Copyright òýÔÔ Carlos Felipe Alcala Perez
Object Description
| Title | Fault diagnosis with reconstruction-based contributions for statistical process monitoring |
| Author | Alcala Perez, Carlos Felipe |
| Author email | alcalape@usc.edu;alcala21@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Chemical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2011-05-03 |
| Date submitted | 2011-07-07 |
| Date approved | 2011-07-07 |
| Restricted until | 2011-07-07 |
| Date published | 2011-07-07 |
| Advisor (committee chair) | Qin, S. Joe |
| Advisor (committee member) |
Tsotsis, Theodore Safonov, Michael G. |
| Abstract | Fault detection and diagnosis are important for the safe operation and control of a process, and for the reduction of its operation and maintenance costs. The implementation of mechanisms for the early detection and diagnosis of faults is called process monitoring. Due to the size and complexity of industrial processes, multivariate statistical methods are finding wide application in process monitoring. Some popular methods are principal component analysis (PCA) for linear processes, and kernel principal component analysis (KPCA) for nonlinear processes. In statistical process monitoring, faults are detected with fault detection indices that trigger alarms when an index has violated its control limit; after a fault is detected, a diagnosis method is used to find its root cause. A popular method for fault diagnosis has been contribution plots. This method uses the idea that a variable with a high contribution to a faulty index is likely the cause of the fault; however, there is no guarantee that a faulty variable would have the largest contribution. For the case of nonlinear processes, very few methods are available for fault diagnosis with KPCA models. ❧ In this dissertation, a new diagnosis method is proposed based on the reconstruction of a detection index along an arbitrary direction. The method is called reconstruction-based contributions (RBC) and is able to provide contributions along single variable, univariate and multivariate directions. ❧ An analysis of the diagnosis power of the RBC method shows that it guarantees correct diagnosis of sensor faults with large magnitudes while the traditional contributions do not. Analysis of the RBC and other diagnosis methods shows that several of these methods can be unified into some general methods; further analysis shows which of them fail to guarantee correct diagnosis for sensor faults with large magnitudes. ❧ The RBC method is extended to the diagnosis of faults in nonlinear processes using KPCA models, where the RBC values are calculated along univariate and multivariate directions using nonlinear optimization methods. ❧ Analysis and simulations show the effectiveness of the RBC method for the diagnosis of simple and complex faults, with univariate or multivariate directions that are known or unknown; and that happen in linear or nonlinear processes. |
| Keyword | statistical process monitoring; principal component analysis; multivariate statistical methods; fault detection; fault diagnosis |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Rights | Alcala Perez, Carlos Felipe |
| Access conditions | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@usc.edu |
| Archival file | uscthesesreloadpub_Volume71/etd-AlcalaPere-59.pdf |
Description
| Title | Page 1 |
| Full text | FAULT DIAGNOSIS WITH RECONSTRUCTION-BASED CONTRIBUTIONS FOR STATISTICAL PROCESS MONITORING by Carlos Felipe Alcala Perez A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Ful llment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (CHEMICAL ENGINEERING) August òýÔÔ Copyright òýÔÔ Carlos Felipe Alcala Perez |
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