采用概率主成分分析的回转支承寿命状态识别

陆超; 陈捷; 洪荣晶

doi:10.7652/xjtuxb201510015

您当前的位置：

首页 >

文章列表页 >

采用概率主成分分析的回转支承寿命状态识别

更新时间：2025-07-09

- 采用概率主成分分析的回转支承寿命状态识别
- Recognition of Life State for Slewing Bearings Using Probabilistic Principal Component Analysis
- 西安交通大学学报 2015年49卷第10期页码：90-96
- 作者机构：
  
  南京工业大学机械与动力工程学院,南京,211800
- 作者简介：
- 基金信息：
  
  : 2015-03-03。作者简介: 陆超(1990—),男,硕士生;洪荣晶(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(51375222)
- DOI：10.7652/xjtuxb201510015
  中图分类号： TH17;TP277
- 网络首发：2015-10-10，
  
  纸质出版：2015
- 稿件说明：
移动端阅览
陆超, 陈捷, 洪荣晶. 采用概率主成分分析的回转支承寿命状态识别[J]. 西安交通大学学报, 2015,49(10):90-96.

Recognition of Life State for Slewing Bearings Using Probabilistic Principal Component Analysis[J]. 2015, 49(10): 90-96.
陆超, 陈捷, 洪荣晶. 采用概率主成分分析的回转支承寿命状态识别[J]. 西安交通大学学报, 2015,49(10):90-96. DOI： 10.7652/xjtuxb201510015.

Recognition of Life State for Slewing Bearings Using Probabilistic Principal Component Analysis[J]. 2015, 49(10): 90-96. DOI： 10.7652/xjtuxb201510015.

摘要

针对回转支承故障特征微弱以及难以提取的特点

提出一种基于概率主成分分析(probabilistic principal component analysis

PPCA)的多领域特征提取方法。该方法从振动信号的时域和时频域中提取出多个能够表征回转支承运行状态的特征向量

并将其组成高维特征集。采用PPCA从高维特征集中提取出最能够反映回转支承寿命状态信息的特征量

将其输入粒子群算法优化的支持向量机中进行寿命状态的识别。通过回转支承全寿命实验证明

基于PPCA的特征提取方法优于传统的主成分分析(principal component analysis

PCA)

其相应的寿命状态识别精度提高了约8%

并且多领域、多变量的特征更能全面反映回转支承的性能退化趋势。与传统的特征提取方法相比

所提方法能够更全面有效地反映复杂恶劣环境下回转支承的故障信息

因此可以用于回转支承的健康监测领域。

Abstract

A novel multi-domain feature extraction approach based on probabilistic principal component analysis(PPCA)is proposed to deal with the weak fault feature of slewing bearings. Several feature vectors are extracted to form a feature set with high dimension. Then the vectors that best reflect the slewing bearing life status are extracted from the feature set by applying PPCA. These vectors are then used as inputs of a support vector machine with particle swarm optimization to perform the life state recognition. It follows from the whole life experiment of slewing bearing that PPCA is better than the traditional PCA in reducing feature dimension

and its recognition accuracy of lifetime state increases by about 8%. A comparison with a single feature or single domain features shows that the multi-domain and multi-feature set reflects the degradation of slewing bearings more comprehensively and accurately. And a comparison with the traditional feature-extraction method shows that the proposed method reflects the fault of the slewing bearing that is running in a complex and harsh environment more effectively

thus

it can be applied in the area of slewing bearing health monitoring.

关键词

Keywords

references

KOSASIH B Y, CAESARENDRA W, TIEU K, et al. Degradation trend estimation and prognosis of large low speed slewing bearing lifetime [J]. Applied Mechanics and Materials, 2014, 493: 343-348.

YANG Yu, YU Dejie, CHENG Junsheng. A roller bearing fault diagnosis method based on EMD energy entropy and ANN [J]. Journal of Sound and Vibration, 2006, 294(1/2): 269-277.

张梅军, 韩思晨, 石文磊, 等. IMF分量的倒频谱分析在滚动轴承故障诊断中的应用 [J]. 液压气动与密封, 2012, 32(3): 33-35.

ZHANG Meijun, HAN Sichen, SHI Wenlei, et al. Cepstrum analysis of the IMF components in ball bearing fault diagnosis [J]. Hydraulics Pneumatics and Seals, 2012, 32(3): 33-35.

DYBALA J, ZIMROZ R. Rolling bearing diagnosing method based on empirical mode decomposition of machine vibration signal [J]. Applied Acoustics, 2014, 77(3): 195-203.

KUMAR H S, SRINIVASA P P, SRIRAM N S, et al. ANN based evaluation of performance of wavelet transform for condition monitoring of rolling element bearing [J]. Procedia Engineering, 2013, 20(2013): 805-814.

HONG Sheng, ZHOU Zheng, ZIO E, et al. Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method [J]. Digital Signal Processing, 2014, 27(2): 159-166.

DONG Shaojiang, LUO Tianhong. Bearing degradation process prediction based on the PCA and optimized LS-SVM model [J]. Measurement, 2013, 46(9): 3143-3152.

刘永前, 徐强, 田德, 等. 基于萤火虫神经网络的轴承性能退化程度评估 [J]. 机械传动, 2014, 38(5): 107-109.

LIU Yongqian, XU Qiang, TIAN De, et al. Bearing performance degradation assessment using optimized BP neural network based on glowworm swarm optimization algorithm [J]. Journal of Mechanical Transmission, 2014, 38(5): 107-109.

CAESARENDRA W, KOSASIH B, TIEU A K, et al. Circular domain features based condition monitoring for low speed slewing bearing [J]. Mechanical Systems and Signal Processing, 2014, 45(1): 114-138.[10] ZVOKELJ M, ZUPAN S, PREBIL I. Multivariate and multiscale monitoring of large-size low-speed bearings using ensemble empirical mode decomposition method combined with principal component analysis [J]. Mechanical Systems and Signal Processing, 2010, 24(4): 1049-1067.

ZVOKELJ M, ZUPAN S, PREBIL I. Non-linear multivariate and multiscale monitoring and signal denoising strategy using kernel principal component analysis combined with ensemble empirical mode decomposition method [J]. Mechanical Systems and Signal Processing, 2011, 25(7): 2631-2653.

ZHONG Guosheng, LI Jiang, ZHAO Kui. Structural safety criteria for blasting vibration based on wavelet packet energy spectra [J]. Mining Science and Technology, 2011, 21(1): 35-40.

张梅军, 王闯, 陈灏. IMF能量和RBF神经网络相结合在滚动轴承故障诊断中的应用研究 [J]. 机械, 2012, 39(6): 63-70.

ZHANG Meijun, WANG Chuang, CHEN Hao. The application research on the combination of IMF energy and RBF neural network in rolling bearing [J]. Machinery, 2012, 39(6): 63-70.

DE MOURA E P, SOUTO C R, SILVA A A, et al. Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses [J]. Mechanical Systems and Signal Processing, 2011, 25(5): 1765-1772.

YU Lingbo, SNAPP R R, RUIZ T, et al. Probabilistic principal component analysis with expectation maximization(PPCA-EM)facilitates volume classification and estimates the missing data [J]. Journal of Structural Biology, 2010, 171(1): 18-30.

罗楠,王泉,刘红霞.一种快速3D打印分层方向确定算法.2015,49(5):140-146.[doi:10.7652/xjtuxb201505 022]

王成,缑锦,白俊卿,等.利用主成分分析的模态参数识别.2013,47(11):97-104.[doi:10.7652/xjtuxb201311018]

白俊卿,闫桂荣,王成.利用局部线性嵌入的模态识别.2013,47(1):85-89.[doi:10.7652/xjtuxb201301017]

吾守尔·斯拉木,曹巨明.一种新的散乱点云尖锐特征提取方法.2012,46(12):1-5.[doi:10.7652/xjtuxb2012120001]

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

面向分类去噪问题的模糊支持向量机新算法

一种新的浮选泡沫图像识别方法

泛化的统一切比雪夫多项式核函数

基于样本熵的注意力相关脑电特征信息提取与分类

一种新的选择性支持向量机集成学习算法