摘要
提出了一种基于神经网络技术的电梯动态智能检测系统,它可以长期不间断地采集电梯运行数据并存储.利用最优小波包的理论分析提取轿厢垂直振动加速度信号特征,与水平方向振动信号特征融合建立了BP神经网络模型,实现对电梯急停故障现象的诊断.经验证,该模型能有效判断出电梯急停故障,为特检部门提供了电梯的检测依据.
An intelligent elevator detecting system based on neural network is proposed in this paper.It can collect and store the running data of elevators continuously. Best wavelet packet basis is used to select the features of car＇s vertical vibration.Along with the horizontal signal features,a BP neural network is made to diagnose the jerk fault.The correction of the model is proved.So the system can provide referential data to the inspectors.
出处
《北京工业大学学报》
EI
CAS
CSCD
北大核心
2010年第4期440-444,共5页
Journal of Beijing University of Technology
基金
北京市科技计划资助项目(Z07050601480701)
关键词
电梯检测
神经网络
最优小波包
elevator detecting
neural network
best wavelet packet
