A relevance vector machine-based approach with application to oil sand pump prognostics.
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.
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. Peter.W.Tse@cityu.edu.hk.
Jinfei
J
Hu
Peter W
PW
Tse
D000465:Algorithms; D004867:Equipment Design; D019544:Equipment Failure Analysis / Q000379:methods; D009821:Oils; D010363:Pattern Recognition, Automated / Q000379:methods*; D012212:Rheology / Q000295:instrumentation*; D012822:Silicon Dioxide; D060388:Support Vector Machine; D014159:Transducers
D009821:Oils; D012822:Silicon Dioxide