Shaft system thermal error modeling method and thermal error compensation system based on SLSTM neural network

Abstract

The invention discloses a shaft system thermal error modeling method based on an SLSTM neural network. The method comprises the following steps: 1) inputting thermal error data of a shaft system changing with time; 2) decomposing the thermal error data into N intrinsic mode components and a residual component by using an EMD algorithm, and respectively converting the component data into a three-dimensional input matrix; 3) encoding the initial time window size, the batch processing size and the unit number of each piece of component data to obtain an original generation bat population; 4) initializing the original generation bat population by adopting a BA algorithm to obtain SLSTM neural networks with different time window sizes, different batch processing sizes and different unit numbers; 5) training the SLSTM neural network by using the thermal error data of the shaft system to determine hyper-parameters; and constructing an EMD-BA-SLSTM network model by using the optimal hyper-parameter, and then reconstructing a prediction component to obtain the output of a prediction result, i.e., the invention also discloses a shaft system thermal error compensation system based on the SLSTM neural network.

Description

technical field [0001] The invention belongs to the technical field of mechanical error analysis, specifically a shaft system thermal error modeling method based on SLSTM neural network and a thermal error compensation system Background technique [0002] The thermal expansion of the shaft system has a hysteresis effect, and the hysteresis effect is significant for the shaft system. Hysteresis effects are important for robust modeling of thermal errors, which lead to time-varying, nonlinear and non-steady-state behavior of thermal expansion temperature behavior. The hysteresis effect means that the current thermally induced error not only depends on the current input, but also has a memory characteristic to the historical thermal effect and is significantly affected by the historical thermal effect. Therefore, the influence of historical thermal information on the current thermal error should be considered in the thermally induced error modeling of the shaft system. Tradit...

AI Technical Summary

Problems solved by technology

Traditional thermal error models cannot apply past thermal information, resulting in poor predictive performance and poor robustness

Images