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Neural networks for condition monitoring and fault diagnosis: the effect of training data on classifier performance

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Parikh, CR, Pont, MJ, Li, Yuhua and Jones, NB (1999) Neural networks for condition monitoring and fault diagnosis: the effect of training data on classifier performance. In: CONDITION MONITORING `99, PROCEEDINGS. UNSPECIFIED. 7 pp. [Conference contribution]

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Abstract

This paper focuses on the development of neural-based condition-monitoring and fault-diagnosis (CMFD) systems. Specifically, we consider the impact of the limited availability of `faulty' training data in real CMFD applications. Where limited data are available we demonstrate two ways in which performance may, in some circumstances, be improved: (1) by using fewer training data made up of roughly equal numbers of,normal' and `fault' samples; or (2) by using a `duplicate-data' training algorithm.

Item Type:Conference contribution (Paper)
Keywords:neural networks; condition monitoring; fault diagnosis; software design
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:5976
Deposited By:Dr Yuhua Li
Deposited On:09 Mar 2010 16:15
Last Modified:09 Mar 2010 16:15

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