Gear pitting fault diagnosis with mixed operating conditions based on adaptive 1D separable convolution with residual connection

Gear pitting fault diagnosis has always been an important subject to industry and research community. In the past, the diagnosis of early gear pitting faults has usually been carried out under single gear health state. In order to diagnose the early gear pitting faults with mixed operating conditions and reduce the number of training parameters, a new method is proposed in this paper. The proposed method uses an adaptive 1D separable convolution with residual connection network to classify gear pitting faults with mixed operating conditions. Compared to the traditional convolutional neural network, the separable convolution with residual connection network can carry out the channel convolution with point-by-point convolution to effectively reduce the number of network parameters. The residual connection can solve the representational bottleneck problem of the features in the model. Moreover, the method proposed in this paper applies the search algorithm to select better hyperparameters of the model. The raw vibration signals of the gear pitting faults at different speeds collected in a gear test rig are used to validate the effectiveness of the proposed method. The results show that the proposed method can accurately diagnose the early gear pitting faults with mixed speeds. In comparison with other machine learning models, the proposed method has provided a better diagnostic accuracy with fewer model parameters.