Skip Nav Destination
Close Modal
Update search
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- ISBN-10
- ISSN
- EISSN
- Issue
- Journal Volume Number
- References
- Conference Volume Title
- Paper No
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- ISBN-10
- ISSN
- EISSN
- Issue
- Journal Volume Number
- References
- Conference Volume Title
- Paper No
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- ISBN-10
- ISSN
- EISSN
- Issue
- Journal Volume Number
- References
- Conference Volume Title
- Paper No
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- ISBN-10
- ISSN
- EISSN
- Issue
- Journal Volume Number
- References
- Conference Volume Title
- Paper No
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- ISBN-10
- ISSN
- EISSN
- Issue
- Journal Volume Number
- References
- Conference Volume Title
- Paper No
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- ISBN-10
- ISSN
- EISSN
- Issue
- Journal Volume Number
- References
- Conference Volume Title
- Paper No
NARROW
Format
Article Type
Subject Area
Topics
Date
Availability
1-1 of 1
Keywords: data-driven fault detection
Close
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Sort by
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Eng. Gas Turbines Power. August 2011, 133(8): 081602.
Published Online: April 6, 2011
...Soumik Sarkar; Xin Jin; Asok Ray An inherent difficulty in sensor-data-driven fault detection is that the detection performance could be drastically reduced under sensor degradation (e.g., drift and noise). Complementary to traditional model-based techniques for fault detection, this paper proposes...