上海交通大学学报

上海交通大学学报（自然版） ›› 2015, Vol. 49 ›› Issue (05): 681-686.

• 机械仪表工程 • 上一篇    下一篇

基于奇异值分解和深度信度网络多分类器的滚动轴承故障诊断方法

李艳峰，王新晴，张梅军，朱会杰   

  1. (解放军理工大学 野战工程学院， 江苏 南京 210007)
  • 收稿日期:2014-06-09
  • 基金资助:

    国家自然科学基金项目（51175511）资助

An Approach to Fault Diagnosis of Rolling Bearing Using SVD and Multiple DBN Classifiers

LI Yanfeng，WANG Xinqing，ZHANG Meijun，ZHU Huijie   

  1. (College of Field Engineering, PLA University of Science and Technology, Nanjing 210007, China)
  • Received:2014-06-09

摘要：

摘要：  提出一种基于奇异值分解(SVD)和深度信度网络(DBN)多分类器的滚动轴承故障诊断方法.对滚动轴承的振动信号进行相空间重构，得到相应的特征矩阵；对特征矩阵进行SVD分解，并用所得整个奇异值序列构造特征向量，建立DBN多分类器模型，以实现滚动轴承的故障诊断；同时，将所提出的方法与DBN、反向传播神经网络、支持向量机等算法进行对比.结果表明，所提出的方法能够更加稳定、可靠地识别滚动轴承的故障类型和故障程度.

关键词: 滚动轴承, 故障诊断, 奇异值分解, 深度信度网络, 多分类器

Abstract:

Abstract： A novel approach to fault diagnosis of rolling bearing using singular value decomposition (SVD) and multiple deep belief network (DBN) classifiers was proposed. According to this approach, vibration signals of rolling bearing under different conditions were reconstructed in the phase space and characteristic matrixes were obtained. Then, the characteristic matrixes were decomposed by SVD to get the singular values. After that, all the singular values were used to form a characteristic vector. Finally, a multiple DBN classifiers model was developed to identify the faults of rolling bearing. To confirm the superiority of the proposed approach, it was compared with DBN, BP neural network and SVM. The experimental results indicates that the proposed approach has a better performance in accuracy and efficiency to identify the fault patterns and severity of rolling bearing.

Key words: rolling bearing, fault diagnosis, singular value decomposition (SVD), deep belief network (DBN), multiple classifiers

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