  Early detection of incipient faults is of vital importance to reducing
maintenance costs, saving energy, and enhancing occupant comfort in buildings.
Popular supervised learning models such as deep neural networks are considered
promising due to their ability to directly learn from labeled fault data;
however, it is known that the performance of supervised learning approaches
highly relies on the availability and quality of labeled training data. In
Fault Detection and Diagnosis (FDD) applications, the lack of labeled incipient
fault data has posed a major challenge to applying these supervised learning
techniques to commercial buildings. To overcome this challenge, this paper
proposes using Monte Carlo dropout (MC-dropout) to enhance the supervised
learning pipeline, so that the resulting neural network is able to detect and
diagnose unseen incipient fault examples. We also examine the proposed
MC-dropout method on the RP-1043 dataset to demonstrate its effectiveness in
indicating the most likely incipient fault types.
