In
the process of industrial production, the equipment is operating
at normal conditions for most of the time, failures are generally
rare. Therefore, the real-world data set collected from industrial
processes always have skewed distributions; the data in normal conditions
are more than that in fault conditions, which result in a class imbalance
(CI) problem. Skewed data distributions will affect the ability of
feature learning of the monitoring model. To overcome the CI problem,
in this paper, a method named double branch rebalanced network (DBRN)
is proposed. DBRN is a two-stage method, which learns two decision
boundaries by designed convolutional neural network branches. First,
in the resampling branch, a novel resampling method is proposed to
rebalance the classes from the data-level. Second, a cost adaptive
reweighting strategy is developed in the reweighting branch to rebalance
the cost of each class. Finally, a fusing learning strategy is designed
to fuse these two branches. To describe the proposed method, the experiment-based
Tennessee Eastman (TE) data set is constructed with different imbalance
ratios. The experiment results show that the proposed DBRN method
achieves better performance in CI fault diagnosis.
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