Learning from Class-imbalanced Data with a Model-Agnostic Framework for Machine Intelligent Diagnosis.

• Time-series augmentation and balanced loss to address minority overfitting. • Data-rebalanced sampler and boundary balancing to address majority domination. • Integrate minority overfitting-based and majority domination-based strategies. • A comprehensive model-agnostic framework for class-imbalanced diagnosis. Considering the difficulty of data acquisition in industry, especially for failure data of large-scale equipment, classification with these class-imbalanced datasets can lead to the problems of minority categories overfitting and majority categories domination. A model-agnostic framework towards class-imbalanced fault diagnosis requirement is proposed to systematically alleviate these problems. Four sub-modules, including Time-series Data Augmentation, Data-Rebalanced sampler, Balanced Margin Loss, and classifier with Dynamic Decision Boundary Balancing are performed to improve recognition accuracy of minority categories without performance degradation on majority categories. Meanwhile, the framework is compatible with general neural networks and provides flexible model candidates to meet the need of feature extraction for different data types. Three case studies on public datasets demonstrate that proposed framework outperformed various state-of-the-art methods. [ABSTRACT FROM AUTHOR]