摘要
自动扶梯的数量在我国呈逐年递增的趋势。定期检测、监督抽查等常见方法通常难以检验自动扶梯内部的潜在故障。提出了一种改进的CNN-LSTM神经网络故障诊断方法,并据此采用LabVIEW设计了自动扶梯监测与故障诊断软件。基于CNN-LSTM神经网络算法,提出将数据浅层特征与深层特征进行融合的改进方法,以提高故障诊断准确率。利用凯斯西储大学数据对提出的故障诊断方法进行实验。结果表明,该故障诊断方法快速有效,针对变工况的故障诊断准确率达到99.4%。基于此,采用LabVIEW设计了自动扶梯监测与故障诊断软件。在自动扶梯关键部件安装多个振动传感器,利用监测软件进行数据采集、数据显示和数据存储。在积累大量工况数据后,可实现典型故障的诊断。
The number of escalators in China has been increasing in recent years.However,regular inspection,supervision,spot check,and other common methods are hardly to inspect the potential failure inside the escalator.An improved fault diagnosis method based on CNN-LSTM neural network is proposed in this paper.Besides,a software for escalator monitoring and fault diagnosis is designed with LabVIEW.Furthermore,an improved method to fuse the shallow and deep data features to improve the accuracy of fault diagnosis based on the CNN-LSTM neural network algorithm is proposed in this paper.The fault diagnosis method proposed is tested with the data of Case Western Reserve University.The results show that the method is efficient and effective,and the fault diagnosis accuracy is 99.4%.Moreover,the software for escalator monitoring and fault diagnosis is designed.there vibration sensors are set on the key parts of the escalator,and monitoring software is used for data acquisition,data display and data storage.After collecting a large amount of operating condition data,the of typical faults can be diagnosed.
作者
谭博韬
黄民
刘跃
安琪
Tan Botao;Huang Min;Liu Yue;An Qi(School of Mechanical Electrical Engineering,Beijing Information Science&Technology University,Beijing 100192,China;Department of Mechanical Engineering,Tsinghua University,Beijing 100084,China)
出处
《电子测量技术》
北大核心
2023年第12期1-7,共7页
Electronic Measurement Technology
基金
国家重点研发计划课题(2020YFB1713205)项目资助。
