ABSTRACT
The lubrication performance of a straight-line conjugate internal meshing gear pump is poor under the low-speed, high-pressure operating conditions of the volumetric servo speed control system, and it is difficult to establish a full fluid lubricating oil film between the gear ring and the housing. This leads to significant wear and severe heating between the gear ring and the housing. The lubrication performance of the interface moving pair of the electro-hydraulic actuator pump gear ring housing can be improved by designing a reasonable lubrication bearing structure for the gear ring housing. In this study, a multi-field coupling multi-objective optimization model was established to improve lubrication performance and volumetric efficiency. The whole model consists of the dynamic model of the gear ring components, the fluid lubrication model of the gear ring housing interface, the oil film formation and sealing model considering the influence of temperature, and the multi-objective optimization model. The comprehensive performance of the straight-line conjugate internal meshing gear pump was verified experimentally using a test bench. The results show that the lubrication performance is improved, the mechanical loss is reduced by 31.52%, and the volumetric efficiency is increased by 4.91%.
ABSTRACT
Due to its advantages of having a high power-to-weight ratio and being energy-efficient, the electro-hydraulic servo pump control system (abbreviated as EHSPCS) is frequently employed in the industrial field, such as the electro-hydraulic servo pump control (EHSPC) servomotor for steam turbine valve regulation control. However, the EHSPCS has strong nonlinearity and time-varying features, and the factors that cause system performance degradation are complex. Once a system failure occurs, it may lead to serious accidents, causing serious casualties and economic losses. To address the above issues, a system health assessment method based on LSTM-GRNN-ANN (LGA) deep neural network is proposed in this paper. Firstly, with oil volume gas content, servo motor air-gap flux density, and system leakage coefficient as the health assessment performance indicators, a health assessment performance index system for the EHSPCS is built, Furthermore, the system performance index threshold is set. Secondly, an LGA deep neural network is constructed by combining LSTM, GRNN and ANN, and a deep neural network based on the LGA is used to create an EHSPCS health assessment model. Subsequently, system feature parameter extraction, algorithm design, and parameter debugging are carried out. Finally, an EHSPCS experimental platform is established, typical system failure simulation experiments are designed, and comparative experimental analysis is conducted. The experimental findings demonstrate that the average accuracy of the system health assessment model based on the LGA deep neural network suggested in this paper is 96.37%, compared to 89.84%, 87.99% for LSTM and GRNN, which validates the accuracy of the system health assessment model based on the LGA deep neural network.
ABSTRACT
An electro-hydraulic servo pump control system (hereinafter referred to as EHSPCS) is a volume servo control unit that is highly integrated with servo motors, fixed-displacement pumps, hydraulic cylinders and functional valve groups. Because of its unique volume direct-drive control mode, the dynamic performance of the system is limited, and the thermal power loss is large, which seriously restricts the improvement of the working quality of the system. To improve the dynamic performance of the system and reduce the thermal power loss to the maximum extent, a multi-objective optimization design method for the EHSPCS is proposed by comprehensively considering the dynamic and efficient energy-saving characteristics of the system. The evaluation model of the dynamic period of the hydraulic cylinder and the thermal power loss of the servo motor are given. Parameters such as the electromagnetic torque of the servo motor, displacement of the hydraulic pump, and working area of the hydraulic cylinder are intelligently optimized by a non-dominated sorting genetic algorithm with elite strategy (NSGA-II). The Pareto front of multi-objective optimization and the corresponding Pareto solution set are obtained; thus, the optimal matching of the system characteristics is realized. Finally, the relevant theory of the multi-objective optimization algorithm is applied to optimize the performance parameters of the hydraulic servo motor, and the prototype is tested in engineering. The experimental results show that the dynamic period of the hydraulic servo motor is accelerated after optimization, and the thermal power loss is significantly reduced. The dynamic and efficient energy-saving characteristics of the system are improved, which further verifies the feasibility of the proposed theory.
