To improve the performance in identifying the faults under strong noise for rotating machinery, this paper presents a dynamic feature reconstruction signal graph method, which plays the key role of the proposed end-to-end fault diagnosis model. Specifically, the original mechanical signal is first decomposed by wavelet packet decomposition (WPD) to obtain multiple subbands including coefficient matrix. Then, with originally defined two feature extraction factors MDD and DDD, a dynamic feature selection method based on L2 energy norm (DFSL) is proposed, which can dynamically select the feature coefficient matrix of WPD based on the difference in the distribution of norm energy, enabling each sub-signal to take adaptive signal reconstruction. Next the coefficient matrices of the optimal feature sub-bands are reconstructed and reorganized to obtain the feature signal graphs. Finally, deep features are extracted from the feature signal graphs by 2D-Convolutional neural network (2D-CNN). Experimental results on a public data platform of a bearing and our laboratory platform of robot grinding show that this method is better than the existing methods under different noise intensities.

翻译：为提升强噪声环境下旋转机械故障识别性能，本文提出一种动态特征重构信号图方法，该方法在所提出的端到端故障诊断模型中起关键作用。具体而言，首先通过小波包分解（WPD）对原始机械信号进行分解，获得包含系数矩阵的多个子带。随后，利用原创定义的两个特征提取因子MDD和DDD，提出一种基于L2能量范数的动态特征选择方法（DFSL），该方法能根据范数能量分布差异动态选择WPD的特征系数矩阵，使各子信号实现自适应信号重构。接着对最优特征子带的系数矩阵进行重构与重组，获得特征信号图。最后通过二维卷积神经网络（2D-CNN）从特征信号图中提取深度特征。在轴承公开数据平台及本实验室机器人磨削平台上的实验结果表明，该方法在不同噪声强度下均优于现有方法。