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Use of artificial intelligence methods for advanced bearing health diagnostics and prognostics

Use of artificial intelligence methods for advanced bearing health diagnostics and prognostics
Use of artificial intelligence methods for advanced bearing health diagnostics and prognostics
Prognostics is the ability to predict the condition of a piece of equipment at any stage during its useful life. It is the cornerstone of Prognostics Health Management (PHM), major goals of which are efficient maintenance and logistical practices, and optimized mission or equipment use and effectiveness. PHM will be achieved through monitoring a range of equipment sub-systems and combining the information to predict how and when the equipment will fail, with sufficient time for action or planning. This paper describes ongoing research by the University of Southampton and GE Aviation to investigate the intelligent processing of mechanical component health data to improve prognostics and diagnostics: In particular to evaluate the effectiveness of various sensing technologies (when applied to monitoring bearings), extending the window of time over which a failing component condition may be determined (prognosing) and identifying the nature of the failure (diagnosing).
artificial Intelligence, bearing health diagnostics, bearing health prognostics
9781424414871
1-9
Institute of Electrical and Electronics Engineers
Chen, S.L.
ffb45732-4c5d-4329-afa3-9cab1c38fd1c
Craig, M.
7f1dfda7-c7ea-4cdb-9010-e103cbe2af42
Callen, R.
5d31222d-1a8b-4f4c-8c4f-482bb234c02c
Powrie, H.
cb7da853-44b6-44be-ba74-6db0ff2e15db
Wood, R.
d9523d31-41a8-459a-8831-70e29ffe8a73
Chen, S.L.
ffb45732-4c5d-4329-afa3-9cab1c38fd1c
Craig, M.
7f1dfda7-c7ea-4cdb-9010-e103cbe2af42
Callen, R.
5d31222d-1a8b-4f4c-8c4f-482bb234c02c
Powrie, H.
cb7da853-44b6-44be-ba74-6db0ff2e15db
Wood, R.
d9523d31-41a8-459a-8831-70e29ffe8a73

Chen, S.L., Craig, M., Callen, R., Powrie, H. and Wood, R. (2008) Use of artificial intelligence methods for advanced bearing health diagnostics and prognostics. In 2008 IEEE Aerospace Conference. Institute of Electrical and Electronics Engineers. pp. 1-9 . (doi:10.1109/AERO.2008.4526604).

Record type: Conference or Workshop Item (Paper)

Abstract

Prognostics is the ability to predict the condition of a piece of equipment at any stage during its useful life. It is the cornerstone of Prognostics Health Management (PHM), major goals of which are efficient maintenance and logistical practices, and optimized mission or equipment use and effectiveness. PHM will be achieved through monitoring a range of equipment sub-systems and combining the information to predict how and when the equipment will fail, with sufficient time for action or planning. This paper describes ongoing research by the University of Southampton and GE Aviation to investigate the intelligent processing of mechanical component health data to improve prognostics and diagnostics: In particular to evaluate the effectiveness of various sensing technologies (when applied to monitoring bearings), extending the window of time over which a failing component condition may be determined (prognosing) and identifying the nature of the failure (diagnosing).

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More information

Published date: 20 May 2008
Additional Information: ISSN 1095-323X
Venue - Dates: 2008 IEEE Aerospace Conference, 2008-03-01 - 2008-03-08
Keywords: artificial Intelligence, bearing health diagnostics, bearing health prognostics

Identifiers

Local EPrints ID: 65425
URI: https://eprints.soton.ac.uk/id/eprint/65425
ISBN: 9781424414871
PURE UUID: d5a98346-3ece-4e39-9f0f-e8861250503b
ORCID for R. Wood: ORCID iD orcid.org/0000-0003-0681-9239

Catalogue record

Date deposited: 11 Feb 2009
Last modified: 06 Jun 2018 13:06

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Contributors

Author: S.L. Chen
Author: M. Craig
Author: R. Callen
Author: H. Powrie
Author: R. Wood ORCID iD

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