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A Combined Markov Chain Model and Generalized Projection Nonnegative Matrix Factorization Approach for Fault Diagnosis
Joint Authors
Niu, Yuguang
Shilin, Wang
Ming, Du
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-06-13
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
The presence of sets of incomplete measurements is a significant issue in the real-world application of multivariate statistical process monitoring models for industrial process fault detection.
Since the missing data in the incomplete measurements are usually correlated with some of the available variables, these measurements can be used if an efficient algorithm is presented.
To resolve the problem, a novel method combining Markov chain model and generalized projection nonnegative matrix factorization (MCM-GPNMF) is proposed to detect and diagnose the faults in industrial process.
The basic idea of the approach is to use MCM-GPNMF to extract the dominant variables from incomplete process data and to combine them with statistical process monitoring techniques.
TG2 and SPEG statistics are defined as online monitoring quantities for fault detection and corresponding contribution plots are also considered for fault isolation.
The proposed method is applied to a 1000 MW unit boiler process.
The simulation results clearly illustrate the feasibility of the proposed method.
American Psychological Association (APA)
Niu, Yuguang& Shilin, Wang& Ming, Du. 2017. A Combined Markov Chain Model and Generalized Projection Nonnegative Matrix Factorization Approach for Fault Diagnosis. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1191652
Modern Language Association (MLA)
Niu, Yuguang…[et al.]. A Combined Markov Chain Model and Generalized Projection Nonnegative Matrix Factorization Approach for Fault Diagnosis. Mathematical Problems in Engineering No. 2017 (2017), pp.1-7.
https://search.emarefa.net/detail/BIM-1191652
American Medical Association (AMA)
Niu, Yuguang& Shilin, Wang& Ming, Du. A Combined Markov Chain Model and Generalized Projection Nonnegative Matrix Factorization Approach for Fault Diagnosis. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1191652
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1191652