一种基于信号特征提取和组合分类的设备故障诊断方法

An equipment fault diagnosis method based on signal feature extraction and combination classification

随着汽车生产线装配输送设备趋向于复杂化和多功能化,设备的日常维护和诊断变得越来越困难,传统的人工诊断和维护效率低,难以满足日益提升的生产需求。为了提高故障诊断准确率,提出了针对汽车总装输送设备的故障诊断框架。根据经验小波变换和奇异值分解提取信号特征,利用信息融合技术,提出一种组合故障诊断方法。通过该方法提升汽车总装输送装备的故障诊断准确率,并将汽车总装输送装备中常用的轴承作为实验案例进行分析,验证了该方法的可行性。

With the tendency of complexity and multi-function,routine maintenance and diagnosis for automobile assembly transport equipment become increasingly difficult.Traditional manual diagnosis and maintenance are inefficient and difficult to meet the increasing production needs.In order to improve the accuracy of fault diagnosis,a fault diagnosis framework for the conveying equipment of automobile assembly is proposed.Based on empirical wavelet transform and singular value decomposition to extract signal features,using information fusion technology,a combined fault diagnosis method is proposed.This method is used to improve the fault diagnosis accuracy rate of automobile assembly transportation equipment,and the commonly used bearings in automobile assembly transportation equipment are analyzed as experimental cases to verify the feasibility of the method.