Multiscale Dynamic Weight-Based Mixed Convolutional Neural Network for Fault Diagnosis of Rotating Machinery

Convolutional neural network (CNN) was widely applied to the data-driven-based fault diagnosis. However, it often needs to artificially transform the signal into a 2-D image with the help of time-frequency transformation; furthermore, the alternative 1-D CNN can only extract single-scale features but cannot adaptively reveal the relationship between scales even if 1-D convolution kernels of multiple scales are applied at the same time. Accordingly, this article proposes a mixed CNN model, namely, a multiscale dynamic weighted 1-D to 2-D (1D–2D) CNN model (1D–2D MDWCNN), which resembles multiwavelet-based CNN method but uses different scale convolution kernels in 1-D convolution neural network to facilitate the multiscale feature extraction and accomplish adaptive features fusion in the constructed 1D–2D seamless joint network framework. Specially, to reflect the global contribution of different convolution kernels, a dynamic weighted (DW) network layer is constructed to adaptively adjust the global weight of each convolution kernel, so as to improve the fault diagnosis ability. The validity of the model is verified by motor bearing fault diagnosis experiment and gearbox fault diagnosis experiment. The diagnosis results proved the developed 1D–2D MDWCNN model superior to the latest CNN-based models.