Abstract
Local damage detection in rotating machinery can become a very difficult issue due to time-varying load or presence of another damage reflected in amplitude modulation of the raw vibration signal. In this paper a two-stage filtering method is presented to deal with this problem. The first stage is based on autoregressive (AR) modeling. It is incorporated to suppress high-energy components that mask an informative signal. High-energy amplitudes of mesh harmonics modulated by other damage or load variation can affect selectors of optimal frequency band as well, so they have to be suppressed. The second stage relies on filtering the AR-residual signal using a linear filter based on an informative frequency band selector. Here as a selector we propose to use the average horizontal distance on quantile-quantile plot. We compare the result of the second stage with the spectral kurtosis. The procedure is illustrated by real data analysis of a two-stage gearbox used in a belt conveyor drive system in an open-pit mine.
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This work is partially supported by the statutory grant No. S30073 (J. Obuchowski).
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Obuchowski, J., Wylomanska, A., Zimroz, R. (2015). Two-Stage Data Driven Filtering for Local Damage Detection in Presence of Time Varying Signal to Noise Ratio. In: Sinha, J. (eds) Vibration Engineering and Technology of Machinery. Mechanisms and Machine Science, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-09918-7_36
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DOI: https://doi.org/10.1007/978-3-319-09918-7_36
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