An Adaptive Oversampling Learning Method for Class-Imbalanced Fault Diagnostics and Prognostics
This addresses data imbalance issues in industrial fault detection, offering an incremental improvement over existing oversampling techniques.
The paper tackles the class-imbalance problem in fault diagnostics and prognostics by proposing an adaptive oversampling method called EWMOTE, which achieves better performance than baseline models on real datasets across binary and multi-class tasks with different imbalance ratios.
Data-driven fault diagnostics and prognostics suffers from class-imbalance problem in industrial systems and it raises challenges to common machine learning algorithms as it becomes difficult to learn the features of the minority class samples. Synthetic oversampling methods are commonly used to tackle these problems by generating the minority class samples to balance the distributions between majority and minority classes. However, many of oversampling methods are inappropriate that they cannot generate effective and useful minority class samples according to different distributions of data, which further complicate the process of learning samples. Thus, this paper proposes a novel adaptive oversampling technique: EM-based Weighted Minority Oversampling TEchnique (EWMOTE) for industrial fault diagnostics and prognostics. The methods comprises a weighted minority sampling strategy to identify hard-to-learn informative minority fault samples and Expectation Maximization (EM) based imputation algorithm to generate fault samples. To validate the performance of the proposed methods, experiments are conducted in two real datasets. The results show that the method could achieve better performance on not only binary class, but multi-class imbalance learning task in different imbalance ratios than other oversampling-based baseline models.