  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.
