针对航空电子设备中倒装芯片缺陷检测振动信号易受噪声影响，缺陷特征不明显等问题，提出一种基于局部重加权卷积稀疏编码（CSC）的方法，以实现倒装芯片振动信号的重构去噪。该方法采用CSC模型全局表示振动信号，能够避免字典高维数问题，以有效降低训练字典和稀疏分解的计算复杂度；其次，针对倒装芯片振动信号稀疏度不同的问题，提出重加权CSC模型，并且为了抑制局部噪声，构建局部重加权CSC模型，在迭代过程中将能量熵以权重的方式重新分配应用于加权策略中，更好地匹配局部块CSC结构；此外，提出随机梯度下降（SGD）的有效加速策略，利用安德森加速（AA）外推法加速SGD算法，对卷积字典历史迭代信息进行线性组合，实现加速卷积字典的学习以及提高字典的求解精度。仿真和实际倒装芯片振动信号试验结果表明，所提方法能够有效地去除倒装芯片振动信号中的噪声，相对于现有流行CSC去噪算法更具竞争性及优越性。

To address the problems that vibration signal of flip-chip defect detection in avionics equipment is easily affected by noise， and the defect characteristics are not obvious， a Convolutional Sparse Coding （CSC） method based on locally reweighted is proposed to reconstruct and de-noise. In this study， CSC model is used to globally represent the vibration signal， so as to avoid the problem of high dictionary dimensions and effectively reduce the computational complexity of training dictionary and sparse decomposition. Secondly， to solve the problem of different sparsity of flip-chip vibration signals， a reweighted CSC model is proposed. In order to suppress local noise， a local reweighted CSC model is constructed. In the iterative process， the energy entropy is redistributed in the way of weights， and applied to the weighted strategy， which can better match the local block CSC structure. In addition， an effective acceleration strategy of Stochastic Gradient Descent （SGD） is proposed， which uses Anderson acceleration （AA） extrapolation method to accelerate the SGD algorithm. This strategy linearly combines the historical iteration information of the convolution dictionary to accelerate the learning of the convolution dictionary and improve the accuracy of dictionary solution. The results of simulation and actual flip chip vibration signal experiments show that the proposed CSC method can effectively remove noise in flip chip vibration signal， and is more competitive and superior to the existing popular CSC denoise algorithms.