Prognostics of slurry pumps based on a moving-average wear degradation index and a general sequential Monte Carlo method

• Published in: Mechanical systems and signal processing Vol. 56-57; pp. 213 - 229
• Main Authors: Wang, Dong, Tse, Peter W.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2015
• Subjects: Degradation; Estimating; Monte Carlo methods; Particle filter; Performance degradation assessment; Performance indices; Prognosis; Pump impellers; Pumps; Remaining useful life; Slurries; Slurry pumps; State space models; Wear; Slurry pumps; Prognosis; Remaining useful life; Particle filter; Performance degradation assessment
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2014.10.010

Slurry pumps are commonly used in oil-sand mining for pumping mixtures of abrasive liquids and solids. These operations cause constant wear of slurry pump impellers, which results in the breakdown of the slurry pumps. This paper develops a prognostic method for estimating remaining useful life of slurry pump impellers. First, a moving-average wear degradation index is proposed to assess the performance degradation of the slurry pump impeller. Secondly, the state space model of the proposed health index is constructed. A general sequential Monte Carlo method is employed to derive the parameters of the state space model. The remaining useful life of the slurry pump impeller is estimated by extrapolating the established state space model to a specified alert threshold. Data collected from an industrial oil sand pump were used to validate the developed method. The results show that the accuracy of the developed method improves as more data become available. •A health index is proposed for impeller performance degradation assessment.•A state space model is constructed to track the evolution of the health index.•A general particle filter is used to infer the parameters of the state space model.•A remaining useful life function is expressed by particles and associated weights.