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

Parikh, CR, Pont, MJ, Li, Y and Jones, NB 1999, Neural networks for condition monitoring and fault diagnosis : The effect of training data on classifier performance , in: International Conference on Condition Monitoring, 12-15 April 1999, Swansea, UK.

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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 or Workshop Item (Paper)
Uncontrolled Keywords: Neural networks, condition monitoring, fault diagnosis, software design
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Proceedings of the International Conference on Condition Monitoring
Refereed: Yes
Funders: Non funded research
Depositing User: Yuhua Li
Date Deposited: 27 Jul 2015 16:56
Last Modified: 05 Apr 2016 18:18
URI: http://usir.salford.ac.uk/id/eprint/33146

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