摘要
滚动轴承早期故障信号具有能量小、频带分布宽等特征,易受到其它能量较大振源信号的干扰。传统的希尔伯特-黄变换(HHT)对信噪比大、多频率调制信号常因不能对其所包含的固有模式函数(IMF)实现准确分离和去除调制干扰分量而失效。本文提出了基于HHT和独立分量分析(ICA)的滚动轴承诊断新方法。该方法首先利用经验模式分解(EMD)将滚动轴承振动信号分解成若干平稳的本征模式函数IMF分量,通过提取若干包含主要信息的IMF分量,应用带通滤波器和Hilbert变换获取IMF分量的高频包络波形,再应用ICA分离包络波形并进行频谱分析,进而判断滚动轴承的运行状况。仿真和试验分析结果验证了本方法的可行性。
Abstract
Vibration generated by incipient faults of rolling element bearing is usually with low energy and dispersed frequency distribution, then, it is easy merged in strong disturbances from other vibration sources. Performing the Hilbert Huang Transition (HHT) directly may fail to remove the noise from the observed modulated signals by separating the Intrinsic Mode Function (IMF) accurately since the vibrations are lower at the signal-to-noise ratio and more frequency modulation. In this paper, a new fault-diagnosis approach is proposed based on the ICA and the HHT. In the approach, the vibration signals are decomposed by the Empirical Mode Decomposition (EMD) to get stable IMF components at first. Then, the IMFs which contain the fault information of the rolling element bearing mostly are selected and the corresponding envelopes are extracted by band-pass filter and the Hilbert transform. Subsequently, the independent component analysis (ICA) is employed to separate the envelopes to independent components (ICs) according to the independent of vibration sources. Finally, the envelope spectrums of the ICs are calculated respectively and compared with the fault characteristics of the rolling element bearing to realize the accurate faults diagnosis of rolling element bearings. Simulations and tests verified the feasibility of the method.
关键词
独立分量分析 /
希尔伯特-黄变换 /
经验模式分解 /
滚动轴承 /
特征提取
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Key words
independent components analysis /
hilbert-huang transition /
empirical mode decomposition /
rolling element bearing /
faults features extraction
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唐先广;郭瑜;丁彦春.
基于独立分量分析与希尔伯特-黄变换的轴承故障特征提取[J]. 振动与冲击, 2011, 30(10): 45-49
TANG Xian-guang;GUO Yu;DING Yan-chun .
Application of hilbert huang transition and independent components analysis on rolling element bearing faults features extraction[J]. Journal of Vibration and Shock, 2011, 30(10): 45-49
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脚注
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