  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.
