Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks
This work addresses the problem of early fault detection in commercial buildings to reduce maintenance costs and save energy, though it is incremental as it builds on existing supervised learning methods.
The paper tackles the challenge of detecting and diagnosing incipient building faults by proposing the use of Monte Carlo dropout to enhance supervised learning pipelines, enabling neural networks to handle unseen fault examples with demonstrated effectiveness on the RP-1043 dataset.
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.