Wear degree prognostics for slurry pumps using support vector machines

Wear damage on impellers is a main cause of the failure of slurry pumps. Prognostics of wear degree allows one to foresee underlying pump failures and thus implement maintenance actions preventively. In this paper, the prediction of wear degree of impellers in slurry pumps is studied. An experimental system is set up to simulate the real working conditions of slurry pumps, from which condition monitoring data and corresponding degrees of impeller damage are collected. An architecture for online prognostics of wear degree is established and an data processing algorithm based on support vector classification is also developed to ensure effective prognostics.