LAUSR

Knowledge transfer with class-imbalanced data is a challenge in predictive maintenance and fault diagnosis. Deep learning algorithms have provided promising results in fault diagnosis. However, their prediction performance is affected by class-imbalanced data in cross-domain tasks. Broad learning algorithms present promising performance in handling class-imbalanced domain-adaptation (CIDA) problems. In the presence of a domain shift, active broad transfer for class-imbalanced learning (ABTCI), an active broad-transfer learning algorithm for CIDA, is proposed. First, the ABTCI algorithm extracts the time–frequency features and feeds them into a recurrent cell to capture spatial–temporal features. Subsequently, it augments the feature space using a sparse autoencoder and an orthogonal mapping projector. By solving the ridge regression problem, the classifier is initialized. Next, the algorithm samples the target data with reliable pseudo-labels and synthesizes new data using random intraclass interpolation among the minor classes containing source and target knowledge. Finally, the classifier is updated using an incremental continuous learning strategy. The performance of the ABTCI algorithm is validated using three datasets, which include 20 class-balanced and 27 class-imbalanced transfer tasks. The performance of the proposed algorithm benchmarked against other deep-learning algorithms is promising.