Augmenting Monte Carlo Dropout Classification Models with Unsupervised Learning Tasks for Detecting and Diagnosing Out-of-Distribution Faults

Understand

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.