Bearing Fault Diagnostics Based on Reconstructed Features
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Liu:2008:ieeeIAS,
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author = "J. Liu and S. Ghafari and W. Wang and
F. Golnaraghi and F. Ismail",
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title = "Bearing Fault Diagnostics Based on Reconstructed
Features",
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booktitle = "IEEE Industry Applications Society Annual Meeting, IAS
'08",
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year = "2008",
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month = oct,
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pages = "1--7",
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keywords = "genetic algorithms, genetic programming, bearing
condition monitoring, bearing fault diagnostic
technique, fault diagnostic reliability, feature
reconstruction, modified kurtosis ratio, one-scale
wavelet analysis, condition monitoring, fault
diagnosis, feature extraction, image reconstruction,
machine bearings, wavelet transforms",
-
DOI = "doi:10.1109/08IAS.2008.173",
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ISSN = "0197-2618",
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abstract = "Rolling-element bearings are widely used in various
mechanical and electrical systems. A reliable bearing
fault diagnostic technique is critically needed in
industries to recognize a bearing fault at its early
stage so as to prevent system's performance degradation
and malfunction. In this work, a genetic programming
based feature reconstruction approach is proposed for
bearing fault diagnostics. A new fitness measure is
proposed to improve the GP operations in feature
formulation. The original features are from the
modified kurtosis ratio and the one-scale wavelet
analysis. Investigation results show that the proposed
method is an effective feature formulation tool; the
reconstructed features are more robust against the
variations in bearing geometry and operating
conditions. The corresponding fault diagnostic
reliability can be enhanced significantly. As a result,
this work provides a promising technique and tool for
bearing condition monitoring for real-world
applications.",
-
notes = "Also known as \cite{4658961}",
- }
Genetic Programming entries for
J Liu
S Ghafari
W Wang
Farid M Golnaraghi
Fathy M Ismail
Citations