基于模态分量的自动扶梯轴承振动故障信号特征提取方法  

Feature Extraction Method of Escalator Bearing Vibration Fault Signal Based on Modal Component

作　　者：李自翔 张强 范豫 LI Zixiang;ZHANG Qiang;FAN Yu(Henan Special Equipment Inspection Technology Research Institute,Zhengzhou 450047,China)

机构地区：[1]河南省特种设备检验技术研究院,河南郑州450047

出　　处：《机械与电子》2024年第11期76-80,共5页Machinery & Electronics

基　　金：河南省市场监督管理局科技计划项目(2023SJ54)。

摘　　要：受自动扶梯轴承振动形态多样性的影响,传统自动扶梯轴承振动故障信号提取方法所提取特征偏差较大,经过长时间实践发现,现阶段所采用的传统振动故障信号提取方法所产生的偏差已经严重影响自动扶梯的正常维护。为此,结合故障信号模量属性引入模态分量计算对其提取过程进行优化,共分为4个步骤:首先对自动扶梯轴承振动位移模态分量变化率进行计算;然后进行故障信号特征的DA模态分解;接着对轴承振动故障特征信号加以识别;最后完成故障信号特征提取。通过与抽取的4种不同提取方法的数据测试分析表明,模态分量算法能够有效提升振动故障信号特征的提取精准度,增强信号中故障分量的权重,改善故障特征提取环境,具有较高的研究价值与市场价值。Influenced by the diversity of vibration forms of escalator bearing,the feature deviation extracted by the traditional vibration fault signal extraction method of escalator bearing is large.After a long time of practice,it is found that the deviation caused by the traditional vibration fault signal extraction method adopted at the present stage has seriously affected the normal maintenance of escalator.Therefore,combining the modulus attribute of the fault signal,modal component calculation is introduced to optimize its extraction process.The method is divided into four steps:first the modal component change rate of escalator bearing is calculated;then the DA mode decomposition of fault signal feature is performed;then the bearing vibration fault feature signal is identified;finally the fault signal feature extraction is completed.The data test analysis with four different extraction methods shows that the modal component algorithm can effectively improve the extraction accuracy of vibration fault signal feature,enhance the weight of the fault component in the signal,and improve the fault feature extraction environment,which has high research value and market value.

关 键 词：模态分量 自动扶梯 轴承故障信号 特征提取 

分 类 号：TH236[机械工程—机械制造及自动化]