基于多因素水平耦合振动模型的高速电梯轿厢系统减振优化设计

IF 2.5 Q2 ENGINEERING, INDUSTRIAL IET Collaborative Intelligent Manufacturing Pub Date : 2024-10-04 DOI:10.1049/cim2.70002
Meihao Chen, Zhaoxi Hong, Junjie Song, Tang Li, Xiuju Song, Yixiong Feng
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引用次数: 0

摘要

随着超高层建筑的兴起,人们对安全舒适的高速电梯的需求日益增长,这促使人们开始关注减振建模和优化问题。本文通过灵敏度评估,筛选出对高速电梯轿厢系统振动有显著影响的因素,形成六维参数空间,并建立了轿厢系统的多目标优化模型。结合 Gibbis 方法和径向基函数神经网络,分别进行采样和构建代用模型。同时,提出了一种 BA-EO 算法,该算法结合了蝙蝠算法和极值优化算法,以适应多维参数空间。在实际应用中,振动加速度的峰峰值对人的感知有很大影响,因此选择峰峰值作为减振优化的目标函数。优化后,轿厢和轿厢框架的振动分别降低了 19% 和 9%,延长了高速电梯的使用寿命,提高了乘客的安全性和舒适性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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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.

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来源期刊
IET Collaborative Intelligent Manufacturing
IET Collaborative Intelligent Manufacturing Engineering-Industrial and Manufacturing Engineering
CiteScore
9.10
自引率
2.40%
发文量
25
审稿时长
20 weeks
期刊介绍: IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly. The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).
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