Oil is a key medium for transmitting power and coupling information in the hydraulic transmission system. Accurate calculation and measurement of the dynamic compressibility of the oil have profound significance to the system performance analysis. The present study primarily focuses on the effect of steady pressure on the bulk modulus of static oil. However, changing pressure and flowing oil are the general working conditions in most of hydraulic apparatus. There are few researchers paying attention to the influence of pressure on the compressibility of flowing oil. Considering the log-normal distribution of bubble size in oil, an improved static oil model (Model B) is developed to calculate the bulk modulus of the motionless oil under the dynamic pressure. Then, by deriving Model B, this paper proposes an original flowing oil model (Model C) to determine the effective bulk modulus of flowing oil. Finally, based on the inherent pressure pulsation of the axial piston pump, an innovative online method for measurement of the bulk modulus of flowing oil is presented as it has the advantage of avoiding interference with flow stability. It has been proved that the changes in the flow velocity corresponds to the crucial effect on the effective bulk modulus of flowing oil, especially under the low-flow and low-pressure operation conditions. Those results and analysis provide promoting support for identifying and determining the effective bulk modulus of oil, analyzing the system stiffness, improving the control accuracy, as well as optimizing the mathematical models. INDEX TERMS Bulk modulus, bubble size distribution, flowing oil, online measurement.

The axial piston pump is a core component for power output conversion in the hydraulic system. Monitoring pump status and diagnosing faults in an optimal way are of profound significance for ensuring system reliability. Currently, considerable studies concentrate primarily on the development of vibration-monitoring technologies. However, due to strong fluid shock and noise, vibration analysis has low signal-to-noise ratio and difficulty in fault location. Therefore, an approach of instantaneous angular speed-based fault detection is introduced in this paper since it has the advantages of short transfer path and non-intrusive measurement. Instantaneous angular speed (IAS) is obtained by cross-period linear interpolation (CLI) algorithm for the voltage square wave induced by a magneto-electric tachometer transducer. Compared with the elapsed time method, CLI has the capacity to precisely capture the speed fluctuation. Weighted angle synchronous averaging (WASA) is then utilized to realize the extraction of abnormal wave components in IAS as a vital signal preprocessing method. Instantaneous angular speed fluctuation (IASF) characteristics of the angular domain are analyzed to study the fault diagnosis under varying working conditions. Moreover, it is proven that after processing, the fault features are extracted in the order spectrum where the deterministic shaft order and its harmonics corresponding to wear characteristics are displayed clearly. Experimental results indicate that IAS has demonstrated more effective and sensitive than vibration signals, thus providing a promising tool for the health monitoring of a pump.

An accurate digital model is of great significance to system operation inversion and behavior prediction. The multi-energy domain coupling mechanism of the Electro-mechanical and Hydraulic (EMH) system is complex and has strong nonlinear characteristics. At present, the research mainly focuses on the mechanical-hydraulic coupling characteristics, while the research on the large operating range and the influence of electric motor and load characteristics on the nonlinear dynamics of the EMH system are less. Based on the first principle description, the nonlinear characteristics of the system components are described in this paper. Furthermore, the nonlinear dynamic model of the EMH system described in multi-state space is established based on the Quasi-LPV system. Combined with the experimental data, the structural and non-structural uncertain parameters of system are identified. Finally, the influence of the mechanical characteristics of electric motor, load on the nonlinear dynamics of the EMH system are discussed. Experiments show that the Quasi-LPV models proposed in this paper can accurately reproduce and predict system behavior. It provides technical support for rapid design, selection, and scheme optimization of hydraulic systems in general scenarios.