Abstract
Centrifugal slurry pumps are widely used in various industries, including Canada’s oil sands industry, to move mixtures of solids and liquids, typically from mine sites to central processing facilities. In highly abrasive applications, such as oil sand slurry, wear of wetted components is the main failure mode of the pumps, and impellers are often the shortest-lived components. An accurate, non-intrusive assessment of component wear in slurry pumps has yet to be developed. This paper will outline a non-destructive vibration-based diagnosis platform based on a novel hypothesis that a specific pattern of vibration – resulting from wear-induced pressure pulsation alteration – can be observed and recorded. Specifically, this method quantifies impeller vane trailing edge damage by analysing the amplitude at the vane passing frequency (VPF) of vibration data. To counter data variability, we employ a combination of three approaches to analyse the acquired vibration data according to the hypothesis.
First, a cumulative amplitude measure was evaluated from VPF amplitudes by employing auto-scaling of time-domain vibration data followed by fast Fourier transform (FFT). Second, an amplitude measure was evaluated from the first component at VPF after utilizing principal component analysis (PCA) on multichannel time-domain data. Finally, an amplitude measure was evaluated from the first component at VPF after utilizing PCA on frequency-domain data. It was found that the final measure had great potential to be used for the identification and estimation of impeller damage due to wear since its values followed the progression of the impeller damage. A viable wear assessment method based on this platform can potentially be used to discern the extent of wear damage on a slurry pump impeller.
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Notes
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Amit S Aulakh and Siyan Wu, Slurry Pump CBM Project, Progress Report 35 (09), Syncrude Canada Ltd., Edmonton, Alberta, Canada, August 21, 2006.
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Mani, G., Wolfe, D., Zhao, X., Zuo, M.J. (2012). Vibration-Based Wear Assessment in Slurry Pumps. In: Amadi-Echendu, J., Willett, R., Brown, K., Mathew, J. (eds) Asset Condition, Information Systems and Decision Models. Engineering Asset Management Review. Springer, London. https://doi.org/10.1007/978-1-4471-2924-0_5
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DOI: https://doi.org/10.1007/978-1-4471-2924-0_5
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