摘要
常规的机械设备轴承故障自动化诊断技术主要使用权重共享函数提取轴承故障特征,易受特征提取层的组成影响,导致诊断效果较差。设计一种全新的机械设备轴承故障自动化诊断技术,结合轴承故障信号阈值进行故障信号降噪处理,利用小波熵设计轴承故障自动化诊断算法,完成机械设备轴承故障自动化诊断。实验结果表明,设计的机械设备轴承故障自动化诊断技术提取的故障特征频率与实际故障特征频率较拟合,诊断效果较好。
The conventional automatic diagnosis technology for bearing faults of mechanical equipment mainly uses the weight sharing functions to extract the bearing fault features,which are easily affected by the composition of the feature extraction layer,resulting in poor diagnostic performance.A new automatic fault diagnosis technology for bearing of mechanical equipment is designed,with the fault signal de-noised according to the bearing fault signal threshold,which uses wavelet entropy to design an automated diagnosis algorithm for bearing faults and complete automated diagnosis of mechanical equipment bearing faults.The experimental results show that the designed automatic diagnosis technology for bearing faults of mechanical equipment extracts fault feature frequencies which match the actual fault feature frequencies and is with good diagnostic effect.
出处
《电动工具》
2023年第4期19-22,共4页
Electric Tool
关键词
机械设备
轴承
故障诊断
降噪
算法
自动化
mechanical equipment
bearing
fault diagnosis
de-noise
algorithm
automation
作者简介
王均佩(1971-),男,大学,研究方向:机械电气。
