Health Monitoring of Centrifugal Pumps Using Digital Models

Abstract

The area of predictive maintenance (PM) has received growing research interest in the past few years. Diagnostic capabilities of PM technologies have increased due to advances made in sensor technologies, signal processing algorithms, and the rapid development of computational power and data handling algorithms. Conventional PM programs are mostly built around analyzing sensors' data collected from physical systems. Incorporating simulation data collected from digital models replicating the physical system with sensors' data can lead to more optimization for operation and maintenance. This paper demonstrates the role of using digital models in implementing effective condition monitoring on centrifugal pumps. Two digital models are used to study the dynamic performance of a centrifugal pump experiencing cavitation condition. The first model is a three-dimensional fully turbulent computational fluid dynamic (CFD) model. Based on the pressure distribution obtained from the CFD, a novel analytical pressure pulsation model is developed and used to simulate the exciting forces affecting the pump. The second digital model is a pump casing dynamic model which is used to predict the casing vibration response to exciting forces due to faulty operating conditions. Results obtained from the digital models are validated using an experimental test rig of a small centrifugal pump. Using this concept, a pump faulty operation can be simulated to provide complete understanding of the root cause of the fault. Additionally, digital models can be used to simulate different corrective actions that would restore the normal operation of the pump.