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中文摘要：
为了提取大型动力设备中被强背景噪声淹没的微弱故障特征,提出一种基于平移不变多小波的相邻系数降噪方法。多小波具有多个尺度函数和小波函数,具备单小波无法同时满足的优良性质,可以匹配信号中不同的特征信息,而平移不变多小波有效地消除了Gibbs现象且其中的平均过程具有优越的消噪性并保持了信号的光滑性。同时相邻系数降噪法考虑小波系数之间的相关性,克服一般阈值消噪的不足。将相邻系数降噪思想引入到平移不变多小波中,并将平移不变多小波相邻系数降噪方法应用于齿轮箱试验台和电力机车滚动轴承的诊断中。齿轮箱试验台和滚动轴承实例的诊断结果表明该方法可以有效地揭示出齿轮早期裂纹的微弱故障特征信息,并成功提取出机车滚动轴承外圈轻微擦伤故障特征频率。
英文摘要：
In order to extract fault features of large-scale power equipment from strong background noise, a new method based on translation-invariant multiwavelets denoising using neighboring coefficients is proposed. Multiwavelets have several scaling functions and wavelet fimctions, possess the excellent properties that scalar wavelet cannot satisfy simultaneously, and match different characteristics of signals. Moreover, translation-invariant multiwavelets avoid Gibbs phenomena and their average process show superior denoising and maintain signal smoothness. Additionally, neighboring coefficient denoising considers relativity of coefficients and overcomes the deficiency of traditional threshold denoising. Therefore, neighboring coefficient denoising is introduced into translation-invariant multiwavelets and translation-invariant multiwavelets denoising using neighboring coefficients is applied to the diagnosis of a gear box and a locomotive rolling bearing. The diagnosis results of the gear box and the rolling bearing show that this method can effectively extract the fault feature of early gear crack, and the fault frequency of slight rub damage of beating outer ring.
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