基于声发射的滚动轴承故障诊断方法

详细信息

Fault Diagnosis Method for Rolling Bearings Based on Acoustic Emission Inspection

摘要: 为诊断低速滚动轴承故障, 克服传统振动法诊断时故障信号极其微弱的缺陷。在实验室条件下对各类故障模式滚动轴承进行声信号采集, 并对故障轴承声信号进行参量分析和波形分析的基础上, 利用撞击数和神经网络技术对滚动轴承进行了故障诊断, 提高了低速滚动轴承故障诊断的有效性和准确性。
- 滚动轴承 /
- 声发射检测 /
- 撞击数 /
- BP神经网络 /
- 小波包分析
Abstract: In order to diagnose the low speed fault rolling bearing and avoid extremely weak signals, it is suggested to use traditional vibration examination method. AE inspection experiments to the different fault pattern of rolling bearings were carried under the laboratory condition. Then the AE signals obtained from rolling bearing were analyzed using parameters and wavelet. Eventually fault patterns of rolling bearing were synthetically judged using hits and neural networks. The validity and the accuracy of low speed bearings fault diagnosis can be enhanced.
- Rolling bearings /
- Acoustic emission testing /
- Hits /
- BP neural networks /
- Wavelet packet analysis

[1]	钟秉林, 黄仁.机械故障诊断学(第二版)[M].北京: 机械工业出版, 2005: 1-10.
[2]	理华, 徐春广, 肖定国, 等. 滚动轴承声发射技术[J].轴承, 2002(7): 24-26.
[3]	赵转哲, 李孟源.铁路货车轴承故障声发射信号的小波分析处理[J].轴承, 2005(3): 30-31.
[4]	Cline J E, Bilodeau J R, Smith R L. Acoustic wayside identification of freight car roller bearing defects[C]// Proceedings of the IEEE/ASME Joint Railroad Conference, [s.l.]: [s.n.], 1998, 79-83.
[5]	沈功田, 段庆儒, 周裕峰, 等.压力容器声发射信号人工神经网络模式识别方法的研究[J].无损检测, 2001, 23(4): 144-149.
[6]	戴光, 李伟, 张颖, 等.基于人工神经网络方法识别声发射信号的有效性[J].大庆石油学院学报, 2001, 25(1): 63-66.
[7]	董长虹.Matlab神经网络与应用[M]．北京: 国防工业出社, 2005.
[8]	于江林, 余永增, 戴光, 等.滚动轴承声发射信号的人工神经网络模式识别技术[J].大庆石油学院学报, 2008, 32(5): 64-66.
[9]	Kwak J, Ha M. Neural network approach for diagnosis of grinding operation by acoustic emission and power signals[J]. Journal of Materials Processing Technology, 2004, 147(1): 65-71.