A NN-based attention model for rope vibration prediction and channel sensitivity analysis

Abstract

It is difficult to implement modeling for overall construction due to the complex structure and distinct environmental influences for the high-speed elevator so researchers rarely achieve end-to-end control for vibration suppression. Meanwhile, the dynamic parameters of the hoist component (the hoist rope) contain more environmental disturbance characteristics than the traction machine. In this paper, a sensitivity analysis of data channels of hoist rope that can help to clarify the effective control loop based on experimental results is implemented. The authors combined a neural network-based (NN-based) regression model and attention module for predicting hoisting rope vibration to achieve over 95% accuracy, and determine the channel data that contributes most to vibration prediction.