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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/7737

Title: Sensor selection in neuro-fuzzy modelling for fault diagnosis
Authors: Zhou, Yimin
Zolotas, Argyrios C.
Issue Date: 2010
Publisher: © IEEE
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.
Abstract: In 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
Description: 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.
Version: Published
DOI: 10.1109/ISIE.2010.5637885
URI: https://dspace.lboro.ac.uk/2134/7737
Publisher Link: http://dx.doi.org/10.1109/ISIE.2010.5637885
ISBN: 9781424463909
Appears in Collections:Conference Papers and Contributions (Mechanical, Electrical and Manufacturing Engineering)

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