Fault diagnosis of low-speed shafts in large gearboxes based on wavelet analysis
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摘要: 針對大型齒輪箱低速軸故障信息難以提取的問題,采用小波分析方法對故障數據進行處理以實現信號在時/頻域的局域性分析,將其無冗余、無泄漏地分解到一組具有緊支撐性的小波基上.文中采用小波分層突變系數作為判別故障隱患的特征值,并對該特征值進行趨勢分析.結果表明:小波變換能有效捕捉沖擊信號的時域特征和故障發生的時間歷程,用小波分層突變系數所做的趨勢圖能有效地預測故障發展趨勢,避免突發故障.Abstract: Aimed at the difficulty to extract the fault information of a low speed shaft in a large gearbox, wavelet analysis was used to realize the local analysis of signals in a time and frequency domain simultaneously. The signals were dissembled to a series of compactly supported wavelet bases non-redundantly and without leaking. The saltation coefficient of wavelet analysis was regarded as a characteristic value to predict a sudden accident and the changing trend of the coefficient was figured out. The results showed that wavelet transform could capture the characteristics in a time domain and the evolvement procedure of a fault. The trend graph of the coefficient could effectively predict the development trend of a fault and avoid a sudden accident.
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Key words:
- wavelet analysis /
- gearbox /
- fault diagnosis /
- characteristic extraction /
- trend graph
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