Abstract
A condition monitoring system (CMS) is a key element in a predictive maintenance strategy allowing to reduce the operating costs of the monitored system. However, the system-driven generation of health indicators requires the knowledge of the system kinematics and the configuration of thresholds which may induce lots of false alarms. In this paper, we propose a generic and data-driven method to automatically generate system health indicators without any a priori knowledge on the monitored system or the acquired signals. The proposed method is based on the automatic detection of spectral content characterising every acquired signal. Within these successive spectral contents, peaks, harmonics series and modulation sidebands are then tracked over time and grouped in time trajectories which will be used to generate the system health indicators.
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Acknowledgments
The KAStrion project has been supported by KIC InnoEnergy, a company supported by the European Institute of Innovation and Technology, and has the mission of delivering commercial products and services, new businesses and innovators in the field of sustainable energy through the integration of higher education, research, entrepreneurs and business companies.
We would also like to thank the CETIM, partner of the project, for providing the signals coming from the wind turbine test rig.
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Gerber, T., Martin, N., Mailhes, C. (2016). Monitoring Based on Time-Frequency Tracking of Estimated Harmonic Series and Modulation Sidebands. 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_7
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DOI: https://doi.org/10.1007/978-3-319-20463-5_7
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