Abstract
Condition monitoring of rotating machinery is a field of intensive research being closely related to the technological evolution in the area of energy, manufacturing and transport. The fault detection and diagnosis of planetary gearbox bearings present an interesting challenge even under steady operating conditions as often the relatively weak bearing signals are masked by other components such as the gear meshing. However, planetary gearboxes usually operate under time varying speed operating conditions (wind turbines, helicopters etc.), complicating even more the diagnostic procedure. The paper focuses on the diagnosis of bearing faults operating under steady and time varying speed conditions applying advanced cyclostationary and cyclo-non-stationary tools. Lately, spectral analysis based on the Cyclic Spectral Coherence (CSCoh) has shown improvement on bearing diagnostics, compared to classical methods, such as the Envelope Analysis. The integration over an optimal band of the CSCoh map leads to the estimation of an enhanced envelope spectrum with strong diagnostic capabilities. While this method seems to work successfully at constant speeds, varying speeds result in cyclo-non-stationary signals, rendering the method unsuccessful. Therefore, re-sampling the signals in the angular domain (using a measured or extracted speed signal) before further processing the signals with CSCoh is used in order to remove the influence of the speed. Furthermore criteria for the selection of the optimal band on the CSCoh (in the angular domain) are proposed and tested. The methodology is applied and evaluated on bearing signals captured over a planetary gearbox operating under steady and time varying speed conditions.
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Mauricio, A., Smith, W., Qi, J., Randall, R., Gryllias, K. (2019). Cyclo-non-stationary Based Bearing Diagnostics of Planetary Gearboxes. In: Fernandez Del Rincon, A., Viadero Rueda, F., Chaari, F., Zimroz, R., Haddar, M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2018. Applied Condition Monitoring, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-030-11220-2_35
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DOI: https://doi.org/10.1007/978-3-030-11220-2_35
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