A NN-based attention model for rope vibration prediction and channel sensitivity analysis. (May 2023)

• Record Type: Journal Article
• Authors: Xu, Shuang; Tao, Jianfeng; Dong, Chang; Mao, Shuai; Xie, Jingren; Liu, Chengliang
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 237:Number 10(2023)
• Page Start: 2316
• Page End: 2327
• Publication Date: 2023-05
• Subjects: Elevator traction system -- hoist rope -- rope dynamics -- neural network -- attention analysis; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/09544062221138829
• Languages: English
• ISSNs: 0954-4062

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.