Global contextual residual convolutional neural networks for motor fault diagnosis under variable-speed conditions

Convolutional neural networks, with a powerful ability for feature representation, have made vast inroads into motor fault diagnosis. However, most of the existing CNN models cannot favorably handle the data generated in variable-speed scenarios. First, the continuous irregular fluctuation of the motor makes the time domain interval between two adjacent fault pulses change continuously. Secondly, due to the complex transmission path of the signal under unstable conditions, the noise distribution is complex. To address this problem, a global contextual residual convolutional neural network is proposed. The major novelties fall into three aspects. First, to make full use of the features from all intermediate layers and explore multiscale information, a new hierarchical structure is adopted in the CNN model. Second, since different features are of different importance for fault detection tasks, the global context module is explored to guide the model to pay more attention to global discriminant features. Third, the features learned by the network can either promote each other or contradict each other, so a multi-feature fusion layer is introduced to integrate these features adaptively. Case studies using the benchmark motor dataset and the industrial motor bearing dataset are performed to validate the superiority of the GC-ResCNN.