基于LSTM特征提取的电梯液压缓冲器隐患识别方法

Elevator hydraulic buffers hidden hazard recognition method based on LSTM feature extraction

作为电梯安全最后保护装置的电梯液压缓冲器,其质量检测非常重要,目前在位现场检测采用人工检测方法,准确度低,存在人身危险。该文提出一种电梯液压缓冲器隐患识别方法总体框架,重点研究基于长短时记忆(long short term memory,LSTM)网络的关键点识别、缓冲器压缩复位特征提取与模式识别技术,以及训练LSTM运动状态识别网络方法。最后,在昱奥GeN2乘客电梯及底坑安装的液压式缓冲器上搭建检测系统进行测试。结果表明:该方法对电梯液压缓冲器常见隐患识别率可达100%,有助于提高检测效率、准确度。

The quality inspection of elevator buffers,which are the last protection device for elevator safety,is very important.Currently,manual inspection methods are used for in-situ inspection,which have low accuracy and personal danger.This paper proposes an overall architecture of an elevator hydraulic buffer hidden danger identification method,focusing on LSTM-based key point identification,buffer compression and reset feature extraction and pattern recognition techniques,and training LSTM operation state identification network methods.Finally,the detection system is built and tested on ZHYO GeN2 passenger elevators and hydraulic buffers installed in the pit,showing that the method can identify up to 100%of the common hidden hazards of elevator hydraulic buffers,which helps improve the efficiency and accuracy of detection.