摘要
Abstract
The traction system is a critical component of an elevator,and its failure during operation can result in significant loss of life and property.To address this,a comprehensive monitoring system that integrates a perception layer,edge processing layer,and cloud service layer,is proposed,along with a fault diagnosis method based on the Long Short-Term Memory network(LSTM).This system enables real-time fault monitoring and diagnosis of the elevator traction system.Field test results from an in-service elevator demonstrate that the average diagnostic response time for the traction system is approximately 0.03 seconds,with an accuracy rate of up to 95.89%.These figures exceed the efficiency and accuracy of traditional manual inspection methods,providing a technical basis for the real-time online health management of critical equipment like elevators.关键词
电梯/曳引系统/故障监测/故障诊断/长短期记忆网络Key words
elevator/traction system/fault monitoring/fault diagnosis/long short term memory network分类
建筑与水利
