Sensor selection in neuro-fuzzy modelling for fault diagnosis

Zhou, Yimin and Zolotas, Argyrios (2010) Sensor selection in neuro-fuzzy modelling for fault diagnosis. In: Industrial Electronics (ISIE), 2010 IEEE International Symposium on, 4-7 July 2010, Bari, Italy.

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Sensor selection in neuro-fuzzy modelling for fault diagnosis

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In this paper, sensor selection relating to neuro-fuzzy 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 is used as an example to illustrate the studied concepts

Keywords:fault diagnosis, fault tolerant issues, input selection, neuro fuzzy modelling, output selection, sensor selection, two tank system, fuzzy control, neurocontrollers, sensors
Subjects:H Engineering > H660 Control Systems
H Engineering > H100 General Engineering
Divisions:College of Science > School of Engineering
ID Code:15059
Deposited On:26 Sep 2014 08:41

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