Sequential Feature-Augmented Deep Multilabel Learning for Compound Fault Diagnosis of Rotating Machinery with Few Labeled and Imbalanced Data

Accurate fault diagnosis of rotating machinery is essential for smooth and safe operations of mechanical systems, and various data-driven methods have been developed based on massive sensing data. However, the frequent occurrence of compound faults makes it much challenging. Meanwhile, the few labeled and imbalanced data of rotating machinery further complicate the design of diagnosis methods. To address these issues, this article proposes a novel sequential feature augmented deep multilabel learning model for compound fault diagnosis. Specifically, by integrating convolutional neural network with convolutional long short-term memory, a deep stacked sparse autoencoder is developed to extract high-dimensional marginal and time-sequential features from few labeled and imbalanced data. Then, a supervised multilabel learning model is developed to learn the relationships among features of single and compound faults and finally realize accurate compound fault diagnosis. Experimental results demonstrated that our model could cope well with few labeled and imbalanced data scenarios and outperforms many existing models.