Abstract
In the paper performance of the adaptive blind deconvolution algorithm in application to a vibration signal with time-varying informative frequency band (IFB) is analyzed. The time-varying nature of the IFB might be caused by e.g. time-varying load or speed, time-varying signal-to-noise ratio (SNR), presence of other damages with distributed nature or time-varying transmission path, especially for source signals that propagate through a rolling element bearing or a planetary gearbox. Linear time-invariant filters cannot follow such phenomena, i.e. they might indicate too wide or too narrow frequency band as informative. Thus, the filtered signal contains too much noise or does not contain the whole information about the damage, respectively. Adaptive blind deconvolution is a time-varying filter which in each step tends to a filter that minimizes or maximizes given criterion of the deconvolved signal. In the classical version it maximizes kurtosis of the deconvolved signal, since high kurtosis (impulsiveness) is expected in the case of local damage. There exist also alternative measures that might provide equivalent results, or sometimes better in specific cases. Such combination of impulsiveness detection and ability of adaptation due to non-stationary operational conditions seems to be very promising. The methodology is illustrated by analysis of real data representing vibration acceleration of a heavy-duty rotating machinery (planetary gearbox used in bucket wheel excavator) operating in industrial conditions of an open-pit mine. The analyzed signal reveals strong dependency between time-varying load applied to the gearbox and properties of cyclic impulses related to damage.
Keywords
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Acknowledgements
This work is partially supported by the statutory grant No. B40044 (J. Obuchowski). This research was supported in part by PL-Grid Infrastructure.
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Obuchowski, J., Wylomanska, A., Zimroz, R. (2016). New Criteria for Adaptive Blind Deconvolution of Vibration Signals from Planetary Gearbox. 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_9
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