基于时间标量指标的滚动轴承振动信号时域分析故障检测

M. Pradhan, Pankaj Gupta
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引用次数: 9

摘要

滚动轴承是旋转机构的关键部件,缺陷的存在可能导致灾难性的失效。在操作过程中及早发现这些缺陷和损坏的严重程度,可以避免故障和故障。故障轴承是振动的来源,其信号可用于故障轴承的评估。提出了通过增大时域信号大小提取故障标量指标进行故障诊断和缺陷严重程度评估的方法。考虑了六个时间标量指标,即峰、均方根、峰因子、峰度、脉冲因子和形状因子。将这些故障指标用于故障严重程度评估的仿真结果进行了比较,发现它们可以用于早期预测。提出了一种新的标量指示器,以提高对轴承故障点的缺陷诊断,并有助于采取必要的措施来避免故障。[2016年8月31日收到;接受2017年2月6日]
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Fault detection using vibration signal analysis of rolling element bearing in time domain using an innovative time scalar indicator
Rolling element bearings are critical components of rotating mechanisms and the presence of defects may cause catastrophic failure. The early identification of such defects with the severity of damage under operating may avoid malfunctioning and breakdown. Defective bearings are the source of vibration and its signals can be used to assess the faulty bearings. The diagnosis of fault and assessment of defect severity using fault scalar indicator extracted from the time domain signal through increase in size has been presented. Six time scalar indicators, namely peak, RMS, crest factor, kurtosis, impulse factor, and shape factor have been considered. The results obtained from these fault indicators for assessing fault severity using simulation have been compared and found that they can be used for early prediction. A new scalar indicator has been developed to improve the diagnosis of defect up to a point of bearing failure and helps to take necessary action to avoid the failure. [Received 31 August 2016; Accepted 06 February 2017]
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