Application of morphological component analysis for rolling element bearing fault diagnosis
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摘要: 滾動軸承局部故障振動信號中的周期性沖擊是識別故障的關鍵特征.形態分量分析在由多種形態原子組成的過完備字典基礎上提取信號中的不同形態成分,基于這種思想提出了一種基于新型過完備復合字典的形態分量分析方法.依據滾動軸承故障振動信號中分量間的形態差異性,改進字典后該方法可以更具針對性地提取出包含故障特征的沖擊分量,配合包絡譜分析準確提取故障特征頻率,診斷滾動軸承局部故障.對比基于快速譜峭度法的軸承故障診斷方法,該方法可以避免人為選擇共振帶產生的不準確性和非最優問題,提高了故障診斷效果.通過軸承仿真信號和故障實驗信號分析驗證了該方法的有效性.Abstract: Periodical impulses in vibration signals are key features in rolling element bearing fault diagnosis. Based on an overcomplete dictionary composed of different morphological atoms, morphological component analysis can be used to extract the signal components of different types of morphologies. A new morphological component analysis method based on a novel over-completed dictionary was proposed herein. According to morphological differences between components in rolling element bearing fault vibration signal, the method after improved dictionary could more targeted to extract impulse components containing fault feature. Then through envelope spectrum analysis, the fault characteristic frequency was extracted accurately, and rolling element bearing local faults were diagnosed. Compared with the Fast Kurtogram method for bearing fault diagnosis, the new method could avoid non-accuracy and non-optimality problems caused by artificial choice of resonance band, and improve the effectiveness of fault diagnosis. By analyzing both the simulation signal and the experimental dataset of rolling element bearing faults, the proposed method is validated.
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Key words:
- rolling element bearing /
- fault diagnosis /
- morphological component analysis /
- impulse
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參考文獻
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