RESUMEN
This paper introduces a fault diagnosis method for mine scraper conveyor gearbox gears using motor current signature analysis (MCSA). This approach solves problems related to gear fault characteristics that are affected by coal flow load and power frequency, which are difficult to extract efficiently. A fault diagnosis method is proposed based on variational mode decomposition (VMD)-Hilbert spectrum and ShuffleNet-V2. Firstly, the gear current signal is decomposed into a series of intrinsic mode functions (IMF) by using VMD, and the sensitive parameters of VMD are optimized by using a genetic algorithm (GA). The Sensitive IMF algorithm judges the modal function sensitive to fault information after VMD processing. By analyzing the local Hilbert instantaneous energy spectrum for fault-sensitive IMF, an accurate expression of signal energy changing with time is obtained to generate the local Hilbert immediate energy spectrum dataset of different fault gears. Finally, ShuffleNet-V2 is used to identify the gear fault state. The experimental results show that the accuracy of the ShuffleNet-V2 neural network is 91.66% after 778 s.
RESUMEN
In the macro/micro dual-drive rotary system, the micro-drive system compensates for the position error of the macro-drive system. To realize the sub-arc-second (i.e., level of 1â³-0.1â³) positioning of the macro/micro dual-drive rotary system, it is necessary to study the positioning performance of the sub-arc-second micro-drive rotary system. In this paper, we designed a sub-arc-second micro-drive rotary system consisting of a PZT (piezoelectric actuator) and a micro rotary mechanism, and used simulation and experimental methods to study the positioning performance of the system. First, the micro-drive rotary system was developed to provide ultra-precise rotary motion. In this system, the PZT has ultrahigh resolution at a level of 0.1 nanometers in linear motion; a micro rotating mechanism was designed according to the composite motion principle of the flexible hinge, which could transform the linear motion of piezoelectric ceramics into rotating motion accurately. Second, the drive performance was analyzed based on the drive performance experiment. Third, kinematics, simulation, and experiments were carried out to analyze the transformation performance of the system. Finally, the positioning performance equation of the system was established based on the two performance equations, and the maximum rotary displacements and positioning error of the system were calculated. The study results showed that the system can provide precision motion at the sub-arc-second and good linearity of motion. This study has a certain reference value in ultra-precision positioning and micromachining for research on rotary motion systems at the sub-arc-second level.
