基于小波消噪技术的轧机轴承早期故障诊断
Incipient Diagnosis on the Bearing Fault of Rolling Mills Based on Wavelet Denoising Technology
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摘要: 由于背景噪声的影响,滚动轴承的冲击故障只有发展到一定程度后,才会在频域中体现明显的倍频特征.因此,直接采用频谱分析无法实现早期故障的特征提取.利用小波变换的“带通滤波”特性,可以将信号按照特定的频段进行分解,分解信号的单支重构可以将噪声与可用信号进行成功分离;采用预先设定的阈值对高频分解系数处理后进行全局重构同样可以达到消噪的目的.针对现场采集的轧机轴承振动信号,采用多种方式消嗓后的信号处理结果表明,含有故障特征的低频信息被成功提取,从消噪信号的频谱图中可以及早辨识故障轴承的特征频率,实现早期故障的精确定位.Abstract: Due to the influence of the strong background noise,the harmonic feature of the shock fault on the rolling bearing can be identified in the frequency field when the fault reaches some severe level.Therefore,the incipient fault feature can not be extracted effectively through frequency spectrum analysis.By using the band pass filtering feature of wavelet transforms,the signal can be decomposed to specific frequency band.Furthermore,the reconstruction of single-level decomposed signal can separate the valuable components from the noise successfully.The same results can be obtained through global reconstruction after threshold denoising on the high frequency decomposition coefficients.The further processing results of the denoisied signal on the rolling mill have showed that the low frequency information containing fault feature is extracted,and the characteristic frequency can be captured from the frequency spectrum of denosied signal.As a result,the incipient fault can be positioned precisely.