Abstract
This paper proposes three algorithms for automatic diagnosis of mechanical system. First of all, an angular resampling with speed measurement correction is introduced. Secondly, a method for the association of detected spectral patterns with the characteristic frequencies of the investigated system is presented. This approach takes into consideration the slippage phenomenon of rolling element bearings. Thirdly, a full-band sideband demodulation method is proposed. It features with multi-rate filtering and offers new health indicators. All methods are applied on the real-world signals of a wind turbine test rig for diagnosis a bearing fault. The comparison of results shows the advantages of the proposed algorithms over well-known health indicators.
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Acknowledgements
All data are provided by CETIM, within KAStrion project, thanks to Sophie Sieg-Zieba.
The Innovation Project KAStrion has been supported by KIC InnoEnergy, which is a company supported by the European Institute of Innovation and Technology (EIT), and has the mission of delivering commercial products and services, new businesses, innovators and entrepreneurs in the field of sustainable energy through the integration of higher education, research, entrepreneurs and business companies. 
This work has been supported by French Research National Agency (ANR) through EITE program (project KAStrion ANR-12-EITE-0002-01). 
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Firla, M., Li, ZY., Martin, N., Barszcz, T. (2016). Automatic and Full-Band Demodulation for Fault Detection—Validation on a Wind Turbine Test Rig. In: Chaari, F., Zimroz, R., Bartelmus, W., Haddar, M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2014. Applied Condition Monitoring, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-20463-5_10
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DOI: https://doi.org/10.1007/978-3-319-20463-5_10
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