This paper deals with the application of speed variable pumps in industrial hydraulic systems. The benefit of the natural feedback of the load torque is investigated for the issue of condition monitoring as the development of losses can be taken as evidence of faults. A new approach is proposed to improve the fault detection capabilities by tracking the changes via machine learning techniques. The presented algorithm is an art of adaptive modeling of the torque balance over a range of steady operation in fault free behavior. The aim thereby is to form a numeric reference with acceptable accuracy of the unit used in particular, taking into consideration the manufacturing tolerances and other operation conditions differences. The learned model gives baseline for identification of major possible abnormalities and offers a fundament for fault isolation by continuously estimating and analyzing the deviations.

This paper describes an investigation into the effects of fluid bulk modulus and traction coefficient properties on piston pump flow losses and radial pison motor torque losses through experimentation, modelling and simulation. Synthetic ester, high bulk modulus, multi-grade, and single grade mineral oils were evaluated. The high bulk modulus fluid exhibited 20% lower pump case and compensator flow losses than a conventional mineral oil of the same viscosity grade. Low traction coefficient fluids reduced the lowspeed torque losses of the radial piston motor by 50%. Physical models for pump case flow and motor torque losses were derived from the experimental data. Field data was collected from a hydraulically propelled agricultural machine. This data was used to model fluid performance in the machine. The simulation results predict that at an operating temperature of 80⁰C, optimizing the bulk modulus and traction coefficients of the fluid could reduce flow losses by 18% and torque losses by 5%. These findings demonstrate the potential of combining comprehensive fluid analysis with modeling and simulation to optimize fluids for the efficient transmission of power.

This paper describes an investigation into the effects of fluid bulk modulus and traction coefficient properties on piston pump flow losses and radial pison motor torque losses through experimentation, modelling and simulation. Synthetic ester, high bulk modulus, multi-grade, and single grade mineral oils were evaluated. The high bulk modulus fluid exhibited 20% lower pump case and compensator flow losses than a conventional mineral oil of the same viscosity grade. Low traction coefficient fluids reduced the lowspeed torque losses of the radial piston motor by 50%. Physical models for pump case flow and motor torque losses were derived from the experimental data. Field data was collected from a hydraulically propelled agricultural machine. This data was used to model fluid performance in the machine. The simulation results predict that at an operating temperature of 80⁰C, optimizing the bulk modulus and traction coefficients of the fluid could reduce flow losses by 18% and torque losses by 5%. These findings demonstrate the potential of combining comprehensive fluid analysis with modeling and simulation to optimize fluids for the efficient transmission of power.

Hydraulics has always been the first choice of actuation in off-road, construction and mining vehicles due to its power density, low cost, built in cooling and lubrication. However, the current state of our environment along with stricter regulations has brought light to newer technologies within hydraulics to improve the existing system. This urge to enhance efficiency and reduce energy consumption has led to a point where new technologies must be evaluated. One such technology is the programmable hydraulic pump called the digital displacement pump (DDP). This new pump may have the potential to revolutionize mobile hydraulics as it can be used to improve part load efficiencies, response and make it easier to control from a system perspective. The DDP is a radial piston pump that has been fit with solenoid on/off valves at the inlet of each cylinder to control the flow of the working fluid. The displacement setting of the pump depends on the displacement of each cylinder controlled digitally by the 'active' inlet valve. The pump can act as a single unit to supply one circuit or it can dedicate pistons for supplying several circuits in parallel using different pump outlet configurations. They can be setup to run in pressure controlled or flow controlled systems to achieve the above mentioned flow sharing capability. An energy study based on two fixed drive cycles (short and intermediate) are conducted on the existing system of a loader used for mining called the ST14 Battery. A breakdown of the energy consumption in the machine is created to look at the impact of the three main actuators (boom, bucket and steering), pump losses and throttling losses have. The losses due to simultaneous load handling and the energy that can be saved by swapping the pumps with a digital displacement pump are also found out and analysed. A model of the existing hydraulic system is made using Simulink and Hopsan using the data and results from the energy study. It will be used to simulate and evaluate future system architectures. This model is then used to simulate a system architecture where the existing pumps are swapped with digital displacement pumps. This architecture is more energy efficient due to the higher energy efficiency of the pump. The findings from the energy study and simulations are compared and results are obtained regarding power losses, energy consumption and overall usability of the models. The addition of the two DDP’s instead of the existing inline pumps has resulted in energy savings resulting in 4% more running time in the intermediate cycle and 5.6% in the short cycle while keeping the functionality of the machine.

This paper deals with the application of speed variable pumps in industrial hydraulic systems. The benefit of the natural feedback of the load torque is investigated for the issue of condition monitoring as the development of losses can be taken as evidence of faults. A new approach is proposed to improve the fault detection capabilities by tracking the changes via machine learning techniques. The presented algorithm is an art of adaptive modeling of the torque balance over a range of steady operation in fault free behavior. The aim thereby is to form a numeric reference with acceptable accuracy of the unit used in particular, taking into consideration the manufacturing tolerances and other operation conditions differences. The learned model gives baseline for identification of major possible abnormalities and offers a fundament for fault isolation by continuously estimating and analyzing the deviations.