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

Parikh, CR; Pont, MJ; Li, Y; Jones, NB

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.

Citation

Parikh, C., Pont, M., Li, Y., & Jones, N. (1999, April). Neural networks for condition monitoring and fault diagnosis : The effect of training data on classifier performance. Presented at International Conference on Condition Monitoring, Swansea, UK

• Presentation Conference Type: Other
• Conference Name: International Conference on Condition Monitoring
• Conference Location: Swansea, UK
• Start Date: Apr 12, 1999
• End Date: Apr 15, 1999
• Publication Date: Apr 1, 1999
• Deposit Date: Jul 27, 2015
• Keywords: Neural networks, condition monitoring, fault diagnosis, software design
• Additional Information: Event Type : Conference