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To extract the global temporal correlations and local features together to enhance the accuracy for fault diagnosis, this paper proposes an effective convolutional Transformer (ECT), which can learn the global temporal correlations using Transformer and local features with convolution at the same time. The proposed method designs a multi-stage hierarchical structure of Transformer, which utilizes convolutional tokenization to distill dominating sequence features from raw vibration signals while increasing the dimension of token embedding across stages at the same time as that in CNNs. The spatial-reduction attention (SRA) and the linear dimension reduction projections are introduced respectively to Transformer at different stages to reduce the resource consumption of the model. Finally, the proposed method utilizes a sequence pooling strategy on the output of Transformer to eliminate the requirement of the class token and make the model accurate for classification. The specially designed structure makes the model flexible and effective for planetary gearbox fault diagnosis. Experiments performed on planetary gearbox fault simulators indicate that the ECT method has significant effectiveness and high accuracy compared with the state-of-the-art methods for planetary gearbox fault diagnosis.

Effective Convolutional Transformer for Highly Accurate Planetary Gearbox Fault Diagnosis