Vibration reduction optimisation design of the high-speed elevator car system based on multi-factor horizontal coupling vibration model

The increasing need for safe and comfortable high-speed elevators due to the rise of super-tall buildings has led to a focus on vibration reduction modelling and optimisation. This article selects factors that have a significant impact on the vibration of high-speed elevator car systems through sensitivity evaluation to form a six-dimensional parameter space and establishes a multi-objective optimisation model for the car system. The Gibbis method and Radial Basis Function neural network are combined to sample and construct surrogate models, respectively. Meanwhile, a BA-EO algorithm that combines Bat algorithm and Extremal optimisation to adapt to a multidimensional parameter space is proposed here. In practical applications, the peak-to-peak value of vibration acceleration, which significantly affects human perception, is chosen as the objective function for vibration reduction optimisation. After optimisation, the vibrations of the car and car frame are decreased by 19% and 9%, respectively, which extend the service life of the high-speed elevator and enhance safety and comfort for passengers. (1) Under the premise of the authenticity and computability of the model, six parameters with the greatest impact on horizontal vibration are selected, and a multi-objective optimisation model for the elevator car system in a six-dimensional complex parameter space is established. (2) Combining the Gibbs method with the Radial Basis Function (RBF) neural network for sampling and establishing a surrogate model can simultaneously meet the rationality of sampling and the accuracy of prediction. (3) Based on the basic principle of the BA algorithm, a BA-EO algorithm based on Le vy flight is proposed. After verification, this algorithm has significant advantages over the BA algorithm in optimising multimodal functions, especially in high-dimensional complex spaces. image