A multi-sensor approach to remaining useful life estimation for a slurry pump. (June 2019) - Tse, Yiu L.; Cholette, Michael E.; Tse, Peter W.
Highlights: Multi-vibration data collected from slurry pump are analyzed with a novel method. RUL are estimated from the suggested method with the help of Kalman Filter. The results show that the suggested method is accurate in the two different cases. Abstract: Slurry pumps handle abrasive and corrosive working fluids and their degradation rate can vary significantly depending on the composition of the slurry, making maintenance scheduling challenging. The paradigm of condition-based maintenance with accurately predicted remaining useful life (RUL) has the potential to significantly save on the total cost of maintenance. In this paper, a new methodology is presented for the RUL estimation for slurry pumps based on the fusion of data emitted from multiple vibration sensors, which enables the construction of a more reliable degradation index. Subsequently, the trend of the new degradation index is predicted using a Kalman Filter to estimate the parameters of a degradation trend line. Finally, an interval estimation of the RUL is obtained by analytically extrapolating the state space model to a pre-defined threshold. The proposed method is deployed to estimate the RUL for a slurry pump in a real production environment with multiple maintenance events, in contrast to previous studies which use limited, single run-to-failure data sets. The results show that the suggested method is capable to predict the RUL of the available datasets, even in the case where one channel is malfunctioning.