优化AR模型的滚动轴承故障IAS信号诊断方法  

Instantaneous angular speed signal based rolling bearing fault diagnosismethod by optimized AR model

作　　者：朱云贵 郭瑜[1] 陈鑫 杨新敏 邹翔 ZHU Yun-gui;GUO Yu;CHEN Xin;YANG Xin-min;ZOU Xiang(Faculty of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学机电工程学院,云南昆明650500

出　　处：《振动工程学报》2024年第12期2141-2147,共7页Journal of Vibration Engineering

基　　金：国家自然科学基金资助项目(52165067);云南省科技计划重大专项(202002AC080001)。

摘　　要：针对振动传感器不易安装场合的滚动轴承故障诊断困难的问题,提出了一种基于优化AR模型的滚动轴承故障瞬时角速度(instantaneous angular speed, IAS)信号诊断方法。采用向前差分法估计获取IAS信号;基于有偏估计自相关分析优化AR模型,依据自相关峭度最大原则确定最优阶次p并进行AR预测剔除IAS信号中的周期分量,获得包含丰富轴承故障信息的残余分量;对残余信号预白化处理使得各频带重要程度同等并包络提取故障特征。通过仿真信号和实测外圈数据验证了所提方法的有效性;试验对比分析结果显示,与现有基于振动信号的快速谱峭度结合阶次分析的方法相比,所提方法的计算效率有显著提高。To address the issue of carrying rolling element bearing(REB)fault diagnosis where the conventional vibration sensor is difficult to install,an instantaneous angular speed(IAS)signal based REB fault diagnosis method by optimized AR model is proposed.The forward differential method is used to calculate and estimate the instantaneous angular speed signal.Then,the biased estimation autocorrelation analysis is used to determine the optimal order p by the maximum autocorrelation kurtosis.Periodic components in the IAS signal are removed by AR prediction,and the residual components containing rich bearing fault information are remained.The residual components are pre-whitened to equalize the importance of each band and to extract fault characteristics from the envelope.Simulation signal and outer ring data from a test rig validate the effectiveness of the proposed method.The experimental comparative analysis results show that the calculation efficiency is improved significantly when compared to the existing method of fast spectral steepness combined with order analysis based on vibration signal.

关 键 词：故障诊断 滚动轴承 AR模型 有偏估计 IAS信号 

分 类 号：TH165.3[机械工程—机械制造及自动化] TH133.33