留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

时域新指标和PNN在滚动轴承故障诊断中的应用

李文峰 戴豪民 许爱强

李文峰, 戴豪民, 许爱强. 时域新指标和PNN在滚动轴承故障诊断中的应用[J]. 机械科学与技术, 2016, 35(9): 1382-1386. doi: 10.13433/j.cnki.1003-8728.2016.0913
引用本文: 李文峰, 戴豪民, 许爱强. 时域新指标和PNN在滚动轴承故障诊断中的应用[J]. 机械科学与技术, 2016, 35(9): 1382-1386. doi: 10.13433/j.cnki.1003-8728.2016.0913
Li Wenfeng, Dai Haomin, Xu Aiqiang. New Time Domain Index and Probabilistic Neural Network and Their Application in Fault Diagnosis of Rolling Bearing[J]. Mechanical Science and Technology for Aerospace Engineering, 2016, 35(9): 1382-1386. doi: 10.13433/j.cnki.1003-8728.2016.0913
Citation: Li Wenfeng, Dai Haomin, Xu Aiqiang. New Time Domain Index and Probabilistic Neural Network and Their Application in Fault Diagnosis of Rolling Bearing[J]. Mechanical Science and Technology for Aerospace Engineering, 2016, 35(9): 1382-1386. doi: 10.13433/j.cnki.1003-8728.2016.0913

时域新指标和PNN在滚动轴承故障诊断中的应用

doi: 10.13433/j.cnki.1003-8728.2016.0913
详细信息
    作者简介:

    李文峰(1983-),助理工程师,博士,研究方向为故障诊断与预测,leoli198389@163.com

New Time Domain Index and Probabilistic Neural Network and Their Application in Fault Diagnosis of Rolling Bearing

  • 摘要: 针对传统时域指标在滚动轴承信号特征提取时分类精度不高的问题。首先,选取适合在线简单快速判别的时域指标,并根据轴承疲劳损伤大小和局部损伤数量增加,分析时域指标对故障的敏感性;其次,融合传统时域指标,得到了两个更为敏感的时域新指标TALAF和THIKAT;最后,利用实时性较好的概率神经网络训练和测试包括两个新指标的数据集,并与未加入新指标的数据集训练和测试结果进行比较,仿真结果验证了TALAF和THIKAT指标有效提高了轴承故障诊断的准确性。
  • [1] 吴斌,王敏杰,康晶,等.滚动轴承故障振动信号特征与诊断方法[J].大连理工大学学报,2013,53(1):76-81 Wu B, Wang M J, Kang J, et al. Fault vibration signal feature of rolling bearing and its diagnosis method[J]. Journal of Dalian University of Technology, 2013,53(1):76-81 (in Chinese)
    [2] 欧璐,于德介.基于监督拉普拉斯分值和主元分析的滚动轴承故障诊断[J].机械工程学报,2014,50(5):88-94 Ou L, Yu D J. Rolling bearing fault diagnosis based on supervised Laplaian score and principal component analysis[J]. Journal of Mechanical Engineering, 2014,50(5):88-94 (in Chinese)
    [3] 东亚斌,廖明夫.具有局部故障的滚动轴承的动力学分析(Ⅱ)内圈具有单一局部故障[J].机械科学与技术,2012,31(5):689-693 Dong Y B, Liao M F. Dynamic analysis on rolling element bearings with localized defects part Ⅱ a single defect in inner race[J]. Mechanical Science and Technology for Aerospace Engineering, 2012,31(5):689-693 (in Chinese)
    [4] 郑近德,程军圣,杨宇.基于LCD和排列熵的滚动轴承故障诊断[J].振动、测试与诊断,2014,34(5):802-806 Zheng J D, Cheng J S, Yang Y. A rolling bearing fault diagnosis method based on LCD and permutation entropy[J]. Journal of Vibration, Measurement & Diagnosis, 2014,34(5):802-806 (in Chinese)
    [5] 徐玉秀,杨文平,吕轩,等.基于支持向量机的汽车发动机故障诊断研究[J].振动与冲击,2013,32(8):143-146 Xu Y X, Yang W P, Lv X, et al. Fault diagnosis for a car engine based on support vector machine[J]. Journal of Vibration and Shock, 2013,32(8):143-146 (in Chinese)
    [6] William P E, Hoffman M W. Identification of bearing faults using time domain zero-crossings[J]. Mechanical Systems and Signal Processing, 2011,25(8):3078-3088
    [7] 程军圣,史美丽,杨宇.基于LMD与神经网络的滚动轴承故障诊断方法[J].振动与冲击,2010,29(8):141-144 Cheng J S, Shi M L, Yang Y. Roller bearing fault diagnosis method based on LMD and neural network[J]. Journal of Vibration and Shock, 2010,29(8):141-144 (in Chinese)
    [8] 钟先友,赵春华,陈保家,等.基于形态自相关和时频切片分析的轴承故障诊断方法[J].振动与冲击,2014,33(4):11-16 Zhong X Y, Zhao C H, Chen B J, et al. Bearing fault diagnosis method based on morphological filtering, time-delayed autocorrelation and time-frequency slice analysis[J]. Journal of Vibration and Shock, 2014,33(4):11-16 (in Chinese)
    [9] 杨国安.滚动轴承故障诊断实用技术[M].北京:中国石化出版社,2012 Yang G A. Fault diagnosis for rolling bearing practical technology[M]. Beijing: China Petrochemical Press, 2012 (in Chinese)
    [10] 樊永生.机械设备诊断的现代信号处理方法[M].北京:国防工业出版社,2009 Fan Y S. Modern signal processing method of mechanical equipment fault diagnosis[M]. Beijing: National Defence Industry Press, 2009 (in Chinese)
    [11] Sassi S B, Badri B, Thomas M. Tracking surface degradation of ball bearings by means of new time domain scalar indicators[J]. International Journal of COMADEM, 2008,11(3):36-45
    [12] 王仲民,周鹏,李充宁.基于改进概率神经网络的滚动轴承故障诊断[J].机械科学与技术,2013,32(5):729-732 Wang Z M, Zhou P, Li C N. Fault diagnosis of roller bearing based on an improved probabilistic neural network[J]. Mechanical Science and Technology for Aerospace Engineering, 2013,32(5):729-732 (in Chinese)
    [13] 张国亮,杨春玲,王暕来.基于优化概率神经网络和红外多光谱融合的大气层外空间弹道目标识别[J].电子与信息学报,2014,36(4):896-902 Zhang G L, Yang C L, Wang J L. Discrimination of exo-atmospheric targets based on optimization of probabilistic neural network and IR multispectral fusion[J]. Journal of Electronics & Information Technology, 2014,36(4):896-902 (in Chinese)
    [14] Lin H T, Liang T J, Chen S M. Estimation of battery state of health using probabilistic neural network[J]. IEEE Transactions on Industrial Informatics, 2013,9(2):679-685
  • 加载中
计量
  • 文章访问数:  243
  • HTML全文浏览量:  24
  • PDF下载量:  9
  • 被引次数: 0
出版历程
  • 收稿日期:  2014-07-22
  • 刊出日期:  2016-09-05

目录

    /

    返回文章
    返回