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Imbalance is a typical characteristic of data in the field of intelligent fault diagnosis. As a data augmentation method that both balances data and extends information, the generative adversarial network has aroused widespread concern in the data imbalance problem. However, it still suffers from high difficulty in model training and poor quality of generated samples. To address this issue, this paper proposes a progressive generative adversarial network, which is a strategy able to gradually generate complex signals from low-frequency signals to wide-frequency signals. In the developed network, on the one hand, decomposing signals into different frequency bands explicitly reduces the difficulty of directly generating high-dimensional and wide-frequency signals. On the other hand, different generative models can focus on different frequency bands, avoiding the interference of high-energy signals on the fault feature frequencies. In order to achieve good generative performance, the generators and training method for the progressive generative adversarial network are elaborately designed. The correlation shortcut avoids information suppression due to the poor selection of noise amplitudes, and the focusing shortcut forces the body of the generator to focus on key feature bands. Ultimately, the superiority of the progressive generative network is verified by evaluating the quality of the generated samples on three datasets and applying the generated samples to solve the data imbalance problem. • A progressive generation framework is proposed to improve the quality of the generated signal. • The proposed generator retains the correlation between low and high frequencies. • The proposed generator can focus on specific frequency bands. • Model at each level is trained independently to avoid the accumulation of errors. [ABSTRACT FROM AUTHOR]

The performance of intelligent fault diagnosis models is often hindered by the lack of available samples, a common issue in both the few-shot learning and imbalanced learning problems. While data generation has been shown to be an effective strategy for addressing this issue, existing methods tend to focus solely on the similarity of the generated samples, overlooking their diversity. In this regard, a self-reasoning training strategy that enables the participation of highly reliable generated samples in the training process is proposed. By augmenting the training samples, the strategy provides the generation model with diverse input information, effectively enhancing the diversity of the samples generated by the model. To assess the reliability of the generated samples, Pearson correlation coefficient between the generated and real samples in the amplitude–frequency domain is utilized. To this end, an amplitude–frequency domain generation model is constructed. In order to avoid issues such as gradient disappearance and convergence degradation during the generation process in the amplitude–frequency domain, a phase-frequency domain generation model as well as a time domain discriminator model are developed. While the phase-frequency domain generation model enhances the diversity of the generated samples in that domain, the time domain discriminator model ensures sample similarity by correlating the amplitude–frequency domain generation model with the phase-frequency domain generation model. Through experiments, it is demonstrated that the proposed method can effectively address the overfitting problem of generative adversarial networks with few samples, improving the diversity of the generated samples. Moreover, the improvement in fault diagnosis performance achieved by the samples generated by the proposed method further underscores its potential and superiority in practical applications. • A self-reasoning training strategy is proposed. • A generation model driven by multi-domain information is developed. • The quality (similarity and diversity) of the generated samples is enhanced. [ABSTRACT FROM AUTHOR]