| 引用本文: | 万书亭,张雄,南冰,张力佳.基于PPCA-1.5维能量谱的滚动轴承故障诊断[J].电力自动化设备,2018,(6): |
| WAN Shuting,ZHANG Xiong,NAN Bing,ZHANG Lijia.Fault diagnosis of rolling bearing based on PPCA and 1.5-dimensional energy spectrum[J].Electric Power Automation Equipment,2018,(6): |
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| 摘要: |
| 针对强背景噪声下滚动轴承的非线性、非平稳故障特征提取不足的问题，提出了融合概率主成分分析(PPCA)及1.5维Teager能量谱的故障特征分析方法。首先对信号进行概率主成分分析，通过对信号降维重构信号，提取信号故障特征主成分，去除强背景噪声干扰；然后对重构信号进行1.5维能量谱分析，从而获得轴承故障特征谱信息。利用所提方法对滚动轴承模拟数据及实验数据进行分析，结果表明与集合经验模态分解(EEMD)包络谱相比，采用PPCA与1.5维能量谱的分析方法在进行滚动轴承故障高阶倍频提取时具有一定的优势。 |
| 关键词: 滚动轴承 概率主成分分析 1.5维能量谱 故障诊断 |
| DOI：10.16081/j.issn.1006-6047.2018.06.025 |
| 分类号:TH212;TH213.3 |
| 基金项目:国家自然科学基金资助项目(51777075)；中央高校基本科研业务费专项资金资助项目(2018QN093) |
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| Fault diagnosis of rolling bearing based on PPCA and 1.5-dimensional energy spectrum |
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WAN Shuting, ZHANG Xiong, NAN Bing, ZHANG Lijia
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Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China
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| Abstract: |
| Aiming at the problem that the nonlinear and non-stationary fault characteristics extraction of the rolling bearing under the strong background noise, the fault diagnosis method is proposed by the combination of PPCA(Probabilistic Principal Component Analysis) and 1.5-dimensional Teager energy spectrum. Firstly, the PPCA of signal is carried out to reduce the dimension of signal, then the signal is reconstructed and its principal fault characteristic component is constructed, and the strong background noise is removed. Then the 1.5-dimensional energy spectrum of the reconstructed signal is analyzed to obtain the characteristic spectrum information of the bearing fault. The proposed method is adopted to analyze the simulative and experimental data of rolling bearing, the results show that compared with EEMD(Ensemble Empirical Mode Decomposition) spectrum envelope, the method combining the PPCA with 1.5-dimensional energy spectrum has certain advantages in high-order frequency extraction of rolling bearing fault. |
| Key words: rolling bearing probabilistic principal component analysis 1.5-dimensional energy spectrum fault diagnosis |
