ZHOU3.pdf (502.6 kB)
Sensor selection in neuro-fuzzy modelling for fault diagnosis
conference contribution
posted on 2011-01-13, 17:26 authored by Yimin Zhou, Argyrios C. ZolotasIn this paper, sensor selection relating to neurofuzzy
modeling for the purpose of fault diagnosis is
discussed. The input/output selection in fuzzy modelling
plays an important role in the performance of the derived
model. In addition, with respect to fault tolerant issues,
the impact of the faults on the system, i.e. possible
incipient and abrupt faults, should be detected in the
earliest possible instance. The paper first presents a brief
introduction to neuro-fuzzy modelling, and proceeds to
sensor selection with the aim of considerably improving
the quality and reliability of the system. We study faults,
both of abrupt and incipient nature, that can be
diagnosed in an immediate sense. A two-tank system
History
School
- Mechanical, Electrical and Manufacturing Engineering
Citation
ZHOU, Y. and ZOLOTAS, A.C., 2010. Sensor selection in neuro-fuzzy modelling for fault diagnosis. IN:IEEE International Symposium on Industrial Electronics (ISIE), Bari, 4-7 July, 7pp.Publisher
© IEEEVersion
- VoR (Version of Record)
Publication date
2010Notes
This is a conference paper [© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.ISBN
9781424463909Publisher version
Language
- en