基于FSDD和MAC的复杂工况滚动轴承在线故障诊断方法

Online fault diagnosis method of rolling bearings with complex working conditions based on FSDD and MAC

环模制粒机的核心零件需在高温、高湿条件下长时间连续运行,极易发生滚动轴承故障,严重影响生产安全。针对复杂工况下环模制粒机滚动轴承故障无法在线诊断的问题,提出了一种基于频域空间分解(FSDD)和模态保证准则(MAC)的滚动轴承故障在线识别方法。首先,对滚动轴承的振动信号进行了在线测量,采集了不同工况下的故障振动数据,并采用均方根(RMS)方法,从不同工况下的振动信号中分析出了振动状况最严重的工况;然后,采用频域空间分解法(FSDD),识别出了其模态频率、阻尼比及振型等模态参数,并利用模态保证准则(MAC)从模态参数中提取出了故障特征频率,达到了损伤判断的目的;最后,以出现故障的K15环模制粒机为例,进行了滚动轴承在线故障诊断的实验。研究结果表明:基于FSDD和MAC的方法,可识别出环模制粒机的故障特征频率为57.83 Hz,故障点为轴承SKF 24024CC/W33的外圈;该方法可实现在复杂工况下滚动轴承故障的有效识别。

The core parts of Pellet Mill need to run continuously for a long time under high temperature and high humidity conditions,which were prone to rolling bearing failures,which seriously affected production safety.Aiming at the problem that rolling bearing faults in the Pellet Mill under complex working conditions cannot be diagnosed online,a rolling bearing online fault diagnosis method based on the frequency and spatial domain decomposition(FSDD)and modal assurance criterion(MAC)was proposed.Firstly the vibration signal was measured online to collect vibration data from Pellet Mill under different working conditions.And the working conditions of Pellet Mill with the most severe vibration conditions were identified by the root mean square(RMS)value of the vibration signal of Pellet Mill under different work conditions.Then,the modal parameters,including frequencies,modal shapes,and damping ratios,were identified by frequency and spatial domain decomposition.And the modal assurance criterion was used to extract the fault characteristic frequency from the identified modal parameters,so the purpose of damage determination was achieved.Finally,the fault diagnosis of a faulty Pellet Mill,whose model is K15,was carried out as an example.The research results show that the fault characteristic frequency of the Pellet Mill is 57.83 Hz,and the fault point is the outer ring of the bearing SKF 24024CC/W33.The proposed method can effectively identify the faults of rolling bearing under complex working conditions.