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Sensors 2013, 13(9), 12663-12686; doi:10.3390/s130912663

A Relevance Vector Machine-Based Approach with Application to Oil Sand Pump Prognostics

The Smart Engineering Asset Management Laboratory (SEAM), Department of Systems Engineering and Engineering Management (SEEM), City University of Hong Kong, Tat Chee Avenue, Kowloon, 999077 Hong Kong
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Received: 18 June 2013 / Revised: 10 September 2013 / Accepted: 12 September 2013 / Published: 18 September 2013
(This article belongs to the Section Physical Sensors)

Abstract

Oil sand pumps are widely used in the mining industry for the delivery of mixtures of abrasive solids and liquids. Because they operate under highly adverse conditions, these pumps usually experience significant wear. Consequently, equipment owners are quite often forced to invest substantially in system maintenance to avoid unscheduled downtime. In this study, an approach combining relevance vector machines (RVMs) with a sum of two exponential functions was developed to predict the remaining useful life (RUL) of field pump impellers. To handle field vibration data, a novel feature extracting process was proposed to arrive at a feature varying with the development of damage in the pump impellers. A case study involving two field datasets demonstrated the effectiveness of the developed method. Compared with standalone exponential fitting, the proposed RVM-based model was much better able to predict the remaining useful life of pump impellers.
Keywords: pump impeller; remaining useful life (RUL); prognosis; relevance vector machine (RVM); sum of two exponential functions pump impeller; remaining useful life (RUL); prognosis; relevance vector machine (RVM); sum of two exponential functions
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Hu, J.; Tse, P.W. A Relevance Vector Machine-Based Approach with Application to Oil Sand Pump Prognostics. Sensors 2013, 13, 12663-12686.

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