China Mechanical Engineering ›› 2021, Vol. 32 ›› Issue (14): 1653-1658,1668.DOI: 10.3969/j.issn.1004-132X.2021.14.003

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A Fault Diagnosis Method of Rotating Machinery Based on LBDP

SHI Mingkuan;ZHAO Rongzhen   

  1. School of Mechanic & Electrical Engineering,Lanzhou University of Technology,Lanzhou,730050
  • Online:2021-07-25 Published:2021-08-04



  1. 兰州理工大学机电工程学院,兰州,730050
  • 通讯作者: 赵荣珍(通信作者),女,1960年生,教授、博士研究生导师。研究方向为旋转机械故障诊断、机械工程动态测试技术、计算智能、机械系统动力学。。
  • 作者简介:石明宽,男,1993年生,硕士研究生。研究方向为旋转机械故障诊断。
  • 基金资助:

Abstract: Aiming at the problems of classification difficulty caused by multi-class and high-dimensional complex characteristics of rotor fault data, a LBDP dimensionality reduction algorithm was proposed. First of all,the mixed features of the rotor vibration signals were extracted from multiple angles in time domain, frequency domain and time-frequency domain,and the high-dimensional feature sets were constructed. The original feature sets were fused by LBDP algorithm, and the low-dimensional sensitive feature subsets which might best reflect the intrinsic information of the faults were selected. Then the low-dimensional feature subsets were input into K-nearest neighbor(KNN) classifier for training and fault classification. The effectiveness of the proposed method was verified by the vibration signal sets of a double-span rotor systems, and it is proved that the method may extract the local discriminant information comprehensively and make the difference among fault categories clearer. 

Key words: locality-balanced discriminant projection(LBDP), dimensionality reduction, fault diagnosis, rotating machinery

摘要: 针对旋转机械故障数据的多类别、高维复杂特性导致的分类困难问题,提出一种基于局部平衡判别投影(LBDP)的故障数据集降维方法。从时域、频域和时频域多个角度提取转子振动信号的混合特征,构建原始高维故障特征集;通过LBDP选择出其中最能反映故障本质的敏感特征子集;将得到的低维特征子集输入到K近邻分类器(KNN)中进行故障模式辨识。通过一个双转子系统的振动信号集合验证了所提出方法的有效性,证明了该方法能够全面地提取出局部判别信息,使故障类别之间的差异性更清晰。

关键词: 局部平衡判别投影, 降维, 故障诊断, 旋转机械

CLC Number: