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Title: Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks
Early detection of incipient faults is of vital im- portance 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.  more » « less
Award ID(s):
1645964
NSF-PAR ID:
10197958
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
2019 IEEE International Conference on Prognostics and Health Management (ICPHM)
Page Range / eLocation ID:
1 to 8
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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