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.
Full text not available from this repository. (Request a copy)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) |
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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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