A lightweight and precision dual track 1D and 2D feature fusion convolutional network for machinery equipment fault diagnosis

Addressing the issues of a single-feature input channel structure, scarcity of training fault data, and insufficient feature learning capabilities in noisy environments for intelligent diagnostic models of mechanical equipment, we propose a method based on a one-dimensional and two-dimensional dual-channel feature information fusion convolutional neural network (1D_2DIFCNN). By constructing a one-dimensional and two-dimensiona dual-channel feature information fusion convolutional network and introducing a Convolutional Block Attention Mechanism, we utilize Random Overlapping Sampling Technique to process raw vibration signals. The model takes as inputs both one-dimensional data and two-dimensional Continuous Wavelet Transform images. Experimental validation shows that this method exhibits faster convergence, higher diagnostic accuracy, and good robustness and generalization performance on two different datasets, outperforming other advanced algorithms.