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

Sensors

Jinfei Hu¹ 

[1] The Smart Engineering Asset Management Laboratory (SEAM), Department of Systems Engineering and Engineering Management (SEEM), City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong 999077, China; E-Mail

关键词: pump impeller;    remaining useful life (RUL);    prognosis;    relevance vector machine (RVM);    sum of two exponential functions;   

DOI  :  10.3390/s130912663

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.