基于数字孪生的电梯制动性能预测及评价

Prediction and Evaluation of Elevator Braking Performance Based on Digital Twin

本文自主设计了电梯实验平台并针对物联网监测电梯的局限性引入数字孪生技术,从而建立了数字孪生电梯制动性能预测及评价系统。通过部署传感器来监测电梯自身的多维数据,并基于智能网关对电梯进行实时动态采集,从而搭建了电梯高保真模型,实现电梯物理实体与孪生体之间的双向映射以及动态交互;基于电梯预测模型并结合PCA-RF-LSTM时间序列的数据回归算法对电梯的性能进行及时分析及故障预测;在电梯的应用服务中搭建决策系统,根据电梯物理实体和虚拟实体的多维数据融合分析以及开发故障诊断、远程运维、事故追溯等分析技术,尝试对电梯制动性能进行故障预测以及安全评价。

This article independently designed an elevator experimental platform and introduced digital twin technology to address the limitations of IoT monitoring elevators,thus establishing a digital twin elevator braking performance prediction and evaluation system.By deploying sensors to monitor the multi-dimensional data of the elevator itself,and dynamically collecting real-time data from the elevator based on an intelligent gateway,a highfidelity model of the elevator has been built,achieving bidirectional mapping and dynamic interaction between the physical entities and twins of the elevator;Based on the elevator prediction model and combined with the PCA-RFLSTM time series data regression algorithm,timely analysis and fault prediction of elevator performance have been made;Building a decision-making system in elevator application services,utilizing multidimensional data fusion analysis of elevator physical and virtual entities,as well as developing analysis technologies such as fault diagnosis,remote operation and maintenance,and accident tracing,this paper attempts to predict the fault and safety evaluation of elevator braking performance.