【24h】

Acoustic Emission Damage Evaluation of Rolling Element Bearings for Shipboard Machinery

机译:船用机械滚动轴承的声发射损伤评估

获取原文

摘要

Rolling element bearings perform an essential role in most rotating machinery. Bearing fault diagnosis and prognosis can detect degradation to bearing performance, preventing the costs of unexpeceted system failure. Acoustic Emission (AE) introduces high sensitivity, early and rapid detection of cracking, and real time monitoring that can provide an alarm once cracking is noticed. This paper discusses the nondestructive monitoring of crack growth in rolling element bearings in a marine environment and the determination of acoustic emission parameters which indicate crack initiation and propagation. The paper's intellectual merit lies in the signal alarm developed from an AE data pattern recognition method, and the specially made rotating machinery test bed that simulates a bearing used on board a ship. Four rolling element bearings were tested in the test bed at various loads and rotation cycles. All AE data was clustered using k-means unsupervised method, and the lowest correlated features were selected for pattern recognition. Useful AE parameters for classifying crack initiation and propagation were determined. Acoustic emission proved to be suitable for remote monitoring of bearing degradation. With the use of signal alarms based upon the clustering method and parameters discussed, one can be notified when a crack has been initiated and is propagating. This will allow the user to avoid a costly unexpected system failure and plan to perform a less costly bearing replacement.
机译:滚动轴承在大多数旋转机械中起着至关重要的作用。轴承故障诊断和预后可以检测到轴承性能下降,从而防止了意外系统故障的代价。声发射(AE)引入了高灵敏度,对裂缝的早期和快速检测以及实时监控,一旦发现裂缝,便可以发出警报。本文讨论了海洋环境中滚动轴承的裂纹扩展的无损监测以及确定裂纹萌生和扩展的声发射参数的确定。本文的知识优势在于通过AE数据模式识别方法开发的信号报警器,以及专门模拟船舶上使用的轴承的旋转机械测试台。在不同的载荷和旋转周期下,在试验台上对四个滚动轴承进行了测试。所有AE数据均使用k-均值无监督方法进行聚类,并选择最低相关特征进行模式识别。确定了用于分类裂纹萌生和扩展的有用AE参数。声发射被证明适合于远程监测轴承的退化。通过使用基于讨论的聚类方法和参数的信号警报,可以通知裂纹何时开始并正在传播。这将使用户避免代价高昂的意外系统故障,并计划执行成本更低的轴承更换。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号