ArXiv Preprint
Deep neural networks (DNNs) are often coupled with physics-based models or
data-driven surrogate models to perform fault detection and health monitoring
of systems in the low data regime. These models serve as digital twins to
generate large quantities of data to train DNNs which would otherwise be
difficult to obtain from the real-life system. However, such models can exhibit
parametric uncertainty that propagates to the generated data. In addition, DNNs
exhibit uncertainty in the parameters learnt during training. In such a
scenario, the performance of the DNN model will be influenced by the
uncertainty in the physics-based model as well as the parameters of the DNN. In
this article, we quantify the impact of both these sources of uncertainty on
the performance of the DNN. We perform explicit propagation of uncertainty in
input data through all layers of the DNN, as well as implicit prediction of
output uncertainty to capture the former. Furthermore, we adopt Monte Carlo
dropout to capture uncertainty in DNN parameters. We demonstrate the approach
for fault detection of power lines with a physics-based model, two types of
input data and three different neural network architectures. We compare the
performance of such uncertainty-aware probabilistic models with their
deterministic counterparts. The results show that the probabilistic models
provide important information regarding the confidence of predictions, while
also delivering an improvement in performance over deterministic models.
Laya Das, Blazhe Gjorgiev, Giovanni Sansavini
2023-03-20
