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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… Click to show full 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.
Journal Title: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
Year Published: 2022
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