Enhanced diagnosis of planetary gear train faults based on bispectrum and attention mechanism deep convolutional generative adversarial networks.

Aiming at the problem of inaccurate fault diagnosis caused by limited fault samples and challenging feature extraction in helicopter planetary gear trains, this study proposes a fault diagnosis method based on Bispectrum and Attention Mechanism Deep Convolutional Generative Adversarial Networks (BAMDCGAN). First, to enhance the sample quality generated by the Attention Mechanism Deep Convolutional Generative Adversarial Network (AMDCGAN), bispectral features are adopted as input samples, forming the proposed BAMDCGAN framework. Secondly, by utilizing the experimental data of the planetary gear train, bispectral feature samples under three load conditions and five fault states were constructed. These samples were used as the training data for BAMDCGAN, enabling the adversarial generation of enhanced fault samples. Finally, the Convolutional Neural Network (CNN) and Vision Transformer (VIT) are trained on the augmented dataset respectively for planetary gear train fault diagnosis. Comparative experiments with Envelope Spectrum + AMDCGAN + CNN, Time Domain + AMDCGAN + CNN, Short-Time Fourier Transform (STFT) + AMDCGAN + CNN, Envelope Spectrum + AMDCGAN + VIT and Hilbert-Huang Transform (HHT) + AMDCGAN + CNN methods demonstrate that the proposed BAMDCGAN-based fault diagnosis method achieves the highest diagnostic accuracy, exceeding 97.8% across varying load conditions. Compared to non-augmented samples, diagnostic accuracy is improved by 2.1%.