Bearing Fault Diagnostics Based on Reconstructed Features

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Liu:2008:ieeeIAS,

• author = "J. Liu and S. Ghafari and W. Wang and F. Golnaraghi and F. Ismail",
• title = "Bearing Fault Diagnostics Based on Reconstructed Features",
• booktitle = "IEEE Industry Applications Society Annual Meeting, IAS '08",
• year = "2008",
• month = oct,
• pages = "1--7",
• 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",
• ISSN = "0197-2618",
• 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