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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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
|Deposited By:||Dr Yuhua Li|
|Deposited On:||09 Mar 2010 16:15|
|Last Modified:||09 May 2016 10:51|
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