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

CR Parikh; MJ Pont; Yuhua Li; NB Jones

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

CR Parikh
, MJ Pont
, Yuhua Li
, NB Jones

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Unknown Host Publication
Pages	237-243
Number of pages	7
Publication status	Published (in print/issue) - 1999
Event	CONDITION MONITORING `99, PROCEEDINGS - Duration: 1 Jan 1999 → …

Conference

Conference	CONDITION MONITORING `99, PROCEEDINGS
Period	1/01/99 → …

Bibliographical note

International Conference on Condition Monitoring, SWANSA, WALES, APR 12-15, 1999

Keywords

neural networks
condition monitoring
fault diagnosis
software design

Cite this

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

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.",

keywords = "neural networks, condition monitoring, fault diagnosis, software design",

author = "CR Parikh and MJ Pont and Yuhua Li and NB Jones",

note = "International Conference on Condition Monitoring, SWANSA, WALES, APR 12-15, 1999; CONDITION MONITORING `99, PROCEEDINGS ; Conference date: 01-01-1999",

year = "1999",

language = "English",

pages = "237--243",

booktitle = "Unknown Host Publication",

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T1 - Neural networks for condition monitoring and fault diagnosis: the effect of training data on classifier performance

AU - Parikh, CR

AU - Pont, MJ

AU - Li, Yuhua

AU - Jones, NB

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PY - 1999

Y1 - 1999

N2 - 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.

AB - 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.

KW - neural networks

KW - condition monitoring

KW - fault diagnosis

KW - software design

M3 - Conference contribution

SP - 237

EP - 243

BT - Unknown Host Publication

T2 - CONDITION MONITORING `99, PROCEEDINGS

Y2 - 1 January 1999

ER -

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

Abstract

Conference

Bibliographical note

Keywords

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Cite this