Intelligent fault diagnosis of planetary gearboxes under time-varying conditions based on dynamic adversarial balance adaptation with multi-label information confusion

Abstract

Intelligent diagnosis methods based on big data have been extensively applied in the fault diagnosis of rotating equipment such as planetary gearboxes. Most of these methods usually satisfy the condition of independent identically distribution among the training and diagnosis data. However, the data distribution in the actual diagnosis task struggles to satisfy the above conditions due to the lack of fault data, missing label information and the feature differences within different signals, thus increasing the difficulty of cross-condition fault diagnosis in small sample sets. Therefore, we propose a dynamic adversarial balance adaptation method with multi-label information confusion (MLC-DABA) for diagnosing planetary gearbox faults under time-varying conditions. In the signal preprocessing process, we transform the nonstationary timing signals into two-dimensional time-frequency matrices for the feature learning of networks, which avoids the frequency characteristic mess caused by frequency overlap. Moreover, we adopt a balance adaptation algorithm to dynamically evaluate the feature distribution between source and target tasks through the domain labels and category labels, thus establishing a balanced adaptation relationship between the feature distributions of both tasks. This dynamic adversarial training mechanism can tap more domain-invariant feature information to measure the distribution distance among tasks, thus closing the feature differences in different tasks and increasing the generalization of source tasks to the data distribution in target tasks. The proposed method is verified in planetary gearbox experiments. Experimental analysis results indicate that the diagnostic performance of MLC-DABA outperforms other comparison methods in terms of accuracy and training robustness.