Data-Driven Adaptive Observer for Fault Diagnosis
Joint Authors
Yin, Shen
Karimi, Hamid Reza
Yang, Xuebo
Source
Mathematical Problems in Engineering
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-21, 21 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-10-24
Country of Publication
Egypt
No. of Pages
21
Main Subjects
Abstract EN
This paper presents an approach for data-driven design of fault diagnosis system.
The proposed fault diagnosis scheme consists of an adaptive residual generator and a bank of isolation observers, whose parameters are directly identified from the process data without identification of complete process model.
To deal with normal variations in the process, the parameters of residual generator are online updated by standard adaptive technique to achieve reliable fault detection performance.
After a fault is successfully detected, the isolation scheme will be activated, in which each isolation observer serves as an indicator corresponding to occurrence of a particular type of fault in the process.
The thresholds can be determined analytically or through estimating the probability density function of related variables.
To illustrate the performance of proposed fault diagnosis approach, a laboratory-scale three-tank system is finally utilized.
It shows that the proposed data-driven scheme is efficient to deal with applications, whose analytical process models are unavailable.
Especially, for the large-scale plants, whose physical models are generally difficult to be established, the proposed approach may offer an effective alternative solution for process monitoring.
American Psychological Association (APA)
Yin, Shen& Yang, Xuebo& Karimi, Hamid Reza. 2012. Data-Driven Adaptive Observer for Fault Diagnosis. Mathematical Problems in Engineering،Vol. 2012, no. 2012, pp.1-21.
https://search.emarefa.net/detail/BIM-1001987
Modern Language Association (MLA)
Yin, Shen…[et al.]. Data-Driven Adaptive Observer for Fault Diagnosis. Mathematical Problems in Engineering No. 2012 (2012), pp.1-21.
https://search.emarefa.net/detail/BIM-1001987
American Medical Association (AMA)
Yin, Shen& Yang, Xuebo& Karimi, Hamid Reza. Data-Driven Adaptive Observer for Fault Diagnosis. Mathematical Problems in Engineering. 2012. Vol. 2012, no. 2012, pp.1-21.
https://search.emarefa.net/detail/BIM-1001987
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1001987