  The Monte Carlo dropout method has proved to be a scalable and easy-to-use
approach for estimating the uncertainty of deep neural network predictions.
This approach was recently applied to Fault Detection and Di-agnosis (FDD)
applications to improve the classification performance on incipient faults. In
this paper, we propose a novel approach of augmenting the classification model
with an additional unsupervised learning task. We justify our choice of
algorithm design via an information-theoretical analysis. Our experimental
results on three datasets from diverse application domains show that the
proposed method leads to improved fault detection and diagnosis performance,
especially on out-of-distribution examples including both incipient and unknown
faults.
