@inproceedings{0d3be84f363f44df800838729ccd1083,
title = "A fault diagnosis method based on composite model and SVM for fermentation process",
abstract = "A method of fault diagnosis based on composite model and support vector machines for fermentation process is proposed to overcome its difficulty in direct measurement of state parameters. In order to obtain the process state, composite model is presented by combining mass equations of bioreactors with RBF neural network that serve as estimators of unmeasured process kinetic parameters. Then Support vector machines are used to analyze and recognize fault patterns, making use of estimated state variables on line. The proposed method is applied to glutamic acid fermentation process, and the simulation results show its feasibility and effectiveness.",
keywords = "Composite model, Fault diagnosis, Fermentation process, Support vector machine",
author = "Liling Ma and Junzheng Wang and Zhigang Liu",
year = "2007",
doi = "10.1109/ICCA.2007.4376933",
language = "English",
isbn = "1424408180",
series = "2007 IEEE International Conference on Control and Automation, ICCA",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3107--3110",
booktitle = "2007 IEEE International Conference on Control and Automation, ICCA",
address = "United States",
note = "2007 IEEE International Conference on Control and Automation, ICCA ; Conference date: 30-05-2007 Through 01-06-2007",
}