A Partial-Label U-Net Learning Method for Compound-Fault Diagnosis With Fault- Sample Class Imbalance

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In the operation process of the rotating machinery, compound faults have various combination forms and are difficult to reproduce, which results in the scarcity of training samples of various compound faults. In this paper, a method of partial-label learning and classification fusion is proposed to investigate the above problems. Wavelet packet transformation (WPT) and dimensionless parameterization (DP) are utilized to extract features in high-dimensional space. A label-specific feature learning architecture is proposed to make up for the shortage of handcrafted features. An improved focal loss is exploited for learning prominent compound-fault characteristics and imbalanced fault samples. Basic probability assignments (BPA) about fault classes are generated from each partial classifier and combined to obtain a final inference about fault types. Experimental results of single-fault and compound-fault classification illustrate the effectiveness of the proposed method.