Deep learning regulation and control and assembly method and device for large-scale high-speed rotary equipment based on dynamic vibration response properties

Abstract

The present invention provides a deep learning regulation and control and assembly method and device for large-scale high-speed rotary equipment based on dynamic vibration response properties. The present invention starts from geometrical deviation of multiple stages of rotor/stator of an aircraft engine, amount of unbalance of rotor/stator, rigidity of rotor/stator and vibration amplitude of rotor/stator, considers the influence of the area of the assembly contact surface between two stages of rotors/stators, and sets the rotation speed of rotor/stator to be the climbing rotation speed to obtain vibration amplitude parameters. According to the calculation method of the coaxiality, amount of unbalance, rigidity and vibration amplitude of multiple stages of rotor/stator, an objective function taking assembly phases as variables is established, a Monte Carlo method is used to solve the objective function, and a probability density function is solved according to a drawn distribution function to obtain the probability relationship between the contact surface runout of the rotor/stator of the aircraft engine and the final coaxiality, amount of unbalance, rigidity and vibration amplitude of multiple stages of rotor/stator, thereby realizing assembly optimization and distribution of tolerances of multiple stages of rotor/stator.