@inproceedings{1f30312e297746eabd766a3dfaf80ea9,
title = "A maximum entropy based approach to fault diagnosis using discrete and continuous features",
abstract = "This paper presents a new maximum entropy (ME) based hybrid inference engine to improve the accuracy of diagnostic decisions using mixed continuous-discrete variables. By fusing the complementary fault information provided by discrete and continuous fault features, false alarms due to misclassification and modeling uncertainty can be significantly reduced. Simulation results using a three-tank benchmark system have clearly illustrated the advantages of diagnostics based on mixed continuous-discrete variables. Moreover, in the presence of significant measurement noise, simulation results show that the proposed ME method achieves better performance than the support vector machine classifier.",
author = "Xiaodong Zhang and David Miller and Roger Xu and Chiman Kwan and Hongda Chen",
note = "Funding Information: This research was supported by the U.S. Office Naval Research under Grant N00014-04-M-0275.",
year = "2006",
doi = "10.3182/20060829-4-cn-2909.00072",
language = "English (US)",
isbn = "9783902661142",
series = "IFAC Proceedings Volumes (IFAC-PapersOnline)",
publisher = "IFAC Secretariat",
number = "PART 1",
pages = "438--443",
booktitle = "6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS 2006",
address = "Austria",
edition = "PART 1",
}