Abstract
Slurry pumps are one of the most common machines in oil sand pumping operations to pump abrasive and erosive solids and liquids from one location to another location. The impeller of a slurry pump is prone to suffer severe wear which may cause slurry pump breakdown and result in huge economic loss. Therefore, it is necessary to construct a health indicator to monitor the health evolution of the impeller. In this paper, raw slurry pump vibration signals are reprocessed through vibration signal analysis and low-pass filtering. Then, multiple statistical features are extracted from time domain and frequency domain, respectively. It should be noted that these statistical features may be correlated and redundant. To reduce the dimensionality of these statistical features, principal component analysis is conducted on these statistical features to discover significant features, namely principal components, for tracking slurry pump health condition. Industrial slurry pump vibration signals are investigated to illustrate how the developed method works. The results show that the deteriorating trend of slurry pump impeller can be well evaluated by the developed method.
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Acknowledgment
The work described in this paper was fully supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CityU 122513) and a grant from the City University of Hong Kong (Project No. 7004251).
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Tse, P.W., Wang, D. (2015). Extraction of Principal Components from Multiple Statistical Features for Slurry Pump Performance Degradation Assessment. In: Amadi-Echendu, J., Hoohlo, C., Mathew, J. (eds) 9th WCEAM Research Papers. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-15536-4_11
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DOI: https://doi.org/10.1007/978-3-319-15536-4_11
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