Enhancing the abilities in assessing slurry pumps' performance degradation and estimating their remaining useful lives by using captured vibration signals

Abstract

Slurry pumps are widely used to transport abrasive slurry that contains oil and sands. Because of the abrasive nature, the impellers inside the pumps wear easily. Severe impeller wear may cause unexpected pump failure that leads to substantial oil production loss. To assess the impeller performance degradation and then estimate its remaining useful life (RUL), an efficient prognostic method has been designed. For assessing the impeller performance degradation, statistical features were extracted from vibration signals collected from on-site operating slurry pumps. Their corresponding frequency spectra were generated after the vibration signals were processed by a low-pass filter. Here, the low-pass filter aims to retain impeller-related vibration components, such as the pump vane-passing frequency and its harmonics. Principal component analysis was then applied to reduce the dimensionality of the extracted statistical features to one dimensionality, which was used to construct a health indicator to reflect the health evolution of the impeller over time. For estimating the impeller's RUL, a nonlinear state space model was designed to track its temporal health indicator. An efficient unscented transform method was employed to iteratively estimate the joint posterior probability density function of the parameters of the nonlinear state space model. After the proper nonlinear state space model had been determined, extrapolations of the nonlinear state space model to a specified alert threshold were used to estimate the impeller's RUL. Vibration signals captured from on-site operating slurry pumps were used to verify the effectiveness of the proposed prognostic method. The results show that prediction accuracy of the estimated RULs have been improved as compared to those generated by other recently developed slurry pump prognostic methods. Moreover, the more the temporal vibration data is available, the better the performance of the state space model; hence, the higher the accuracy in predicting the impeller's RUL.