In order to efficiently extract weak fault features of key equipments immersed in strong background noise, a multi-wavelet denoising method with adaptive block thresholding is proposed and it is applied to gearbox fault diagnosis of the rolling mills. The effect of wavelet denoising mainly depends on the optimal selection of wavelet functions and threshold. Multiwave-lets have more than two multiscaling functions and multiwavelet functions. They possess such properties as orthogonality, symmetry, compact support and high vanishing moments simultaneously. Therefore, multiwavelets are extensively used for fault diagnosis of incipient faults and weak faults. Based on the correlation of multiwavelet coefficients, this paper uses the minimum principle of Stein's unbiased risk estimate to estimate the true fault features. The optimal block length and threshold are selected for effective feature extraction and noise elimination at each decomposition level. The simulation signal validates the effectiveness of the proposed method, the gearbox fault diagnosis of the rolling mills indicates that the proposed method can successfully detect two local scuffing fault features of the pinion simultaneously.%为有效提取关键设备淹没在强背景噪声中的微弱故障特征,提出了一种多小波自适应分块阈值降噪方法,并将其成功应用于轧机齿轮箱故障诊断中.小波降噪的效果主要取决于小波函数和阈值的合理选择.多小波具有多个尺度函数和小波函数,可以同时满足紧支性、对称性、正交性以及高阶消失矩等优良性质,使其在早期故障和微弱故障诊断中颇具优势.针对多小波变换系数之间的相关性,在估计真实特征值时以Stein无偏风险估计最小作为约束条件,自适应地选取最优的邻域分块长度和阈值,能够在准确提取故障特征的同时有效消除噪声干扰.仿真信号验证了方法的有效性,轧机齿轮箱的诊断结果表明,该方法可以有效提取出齿轮箱高速小齿轮存在由于高温熔焊导致的两处局部胶合破坏故障.
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