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Design analysis and simulation of an external helical gear
이철희,양진룽,이강희,이철희 사단법인 유공압건설기계학회 2023 드라이브·컨트롤 Vol.20 No.4
This study optimized the parameters of the helical gear based on the original external meshing helical gear pump, combined with the analysis of the stability and flow of the basic parameters of the equipment; herringbone gears were used to eliminate the axial force generated by the helical gears. An optimized helical gear rotor was built with NX. The error between the simulation and calculation results of pump displacement was 3.95% and the simulation results were valid. Analysis of the outlet pressure and lift changes (maximum change rates of 0.38% and 0.25%), pressure analysis of the XY center plane at different times in the same cycle (no pressure surge or drop), and analysis of the axial force of the primary and driven rotors (axis The axial force is close to 0) were performed. The results showed that the flow pulsation of the external gear pump was slight, the operation was smooth, vibration and friction were reduced, the wear of bearings and other components could be diminished, and the service life of the equipment was extended. The simulation results showed that the external gear pump met the design requirements.
Reliable Fault Diagnosis Method Based on An Optimized Deep Belief Network for Gearbox
이철희,Oybek Eraliev,Ozodbek Xakimov,이철희 사단법인 유공압건설기계학회 2023 드라이브·컨트롤 Vol.20 No.4
High and intermittent loading cycles induce fatigue damage to transmission components, resulting in premature gearbox failure. To identify gearbox defects, numerous vibration-based diagnostics techniques, using several artificial intelligence (AI) algorithms, have recently been presented. In this paper, an optimized deep belief network (DBN) model for gearbox problem diagnosis was designed based on time-frequency visual pattern identification. To optimize the hyperparameters of the model, a particle swarm optimization (PSO) approach was integrated into the DBN. The proposed model was tested on two gearbox datasets: a wind turbine gearbox and an experimental gearbox. The optimized DBN model demonstrated strong and robust performance in classification accuracy. In addition, the accuracy of the generated datasets was compared using traditional ML and DL algorithms. Furthermore, the proposed model was evaluated on different partitions of the dataset. The results showed that, even with a small amount of sample data, the optimized DBN model achieved high accuracy in diagnosis