Hydraulic pump health monitoring can give early notice of a catastrophic failure occurring within the pump, saving time and money on repairs. This work focuses on developing a system to monitor an axial piston pump’s volumetric efficiency, using state and parameter estimation techniques. A high order, nonlinear model has been utilized for the axial piston pump. Pressure measurements of the pump are used for a linear Kalman filter (KF) as well as an extended Kalman filter (EKF) to estimate the remaining states of pump model. Volumetric efficiency losses are tracked by the filters via estimation of two flow loss coefficients, low Reynolds and high Reynolds flow loss, which are allowed to vary within the model to track the changes. In a separate analysis, a third parameter, a disturbance torque, was applied to the load and its estimation in a similar process to the flow loss coefficients.
Both filters are able to estimate a single flow loss or load. However, the KF was unable to distinguish between two flow losses. The EKF is able to distinguish between low and high Reynolds number flows since it takes into account the nonlinearities in the system including the flow loss characteristics. The EKF shows promise in being able to estimate both flow losses and a load disturbance simultaneously. Both types of filters are found to have fast run times suggesting that the filters could be implemented using typical microcontroller hardware found on industrial and mobile hydraulic machinery.
