Data-driven lean blowout prediction based on industrial dry low emission gas turbine dataset Using decision tree
Author:
Affiliation:
1. Universiti Teknologi Petronas Perak,dept. Electrical and Electronics Engineering,Malaysia
Funder
Ministry of Higher Education
PETRONAS
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9951708/9951713/09951749.pdf?arnumber=9951749
Reference21 articles.
1. Use of Flame Color and Chemiluminescence for Early Detection of Lean Blowout in Gas Turbine Combustors at Different Levels of Fuel–Air Premixing
2. Early detection of lean blowout in a combustor using symbolic analysis of colour images
3. Detection and classification of lean blow-out and thermoacoustic instability in turbulent combustors
4. Real-time prediction of lean blowout using chemical reactor network
5. Prevention of lean flame blowout using a predictive chemical reactor network control
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1. Lube Based Oil Unit Anomaly Limit Prediction using Decision Tree Classification Model;2024 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS);2024-06-29
2. Supervised learning-based multi-site lean blowout prediction for dry low emission gas turbine;Expert Systems with Applications;2024-06
3. Real-Time UAV System Integration for Fire Detection and Classification;2023 IEEE International Conference on Computing (ICOCO);2023-10-09
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