“Preventive Maintenance of Centralized HVAC Systems: Use of Acoustic Sensors, Feature Extraction, and Unsupervised Learning” by Ravi Srinivasan, Tamzeed Islam, Bashima Islam, Zeyu Wang, Tamim Sookoor, Nirjon Shahriar, and Omprakash Gnawali. In Proceedings of the 15th biennial International Building Performance Simulation Association Building Simulation Conference (BuildingSimulation 2017), Aug. 2017.
In this paper, we propose a predictive maintenance scheme for centralized HVAC systems by autonomous monitoring and analyzing their acoustic emissions. Our proposed solution allows a building to be retrofitted to monitor its HVAC without having to modify the existing infrastructure. Our approach is to employ an energy-efficient, low-cost, and distributed acoustic sensing platform to capture and process audio signals from HVAC systems. As part of this project, we develop audio models of a running HVAC system using a combination of unsupervised and supervised machine learning techniques with a human-in-the-loop for fault identification and prediction.
BibTeX entry:
@inproceedings{hvacacoustics-buildsim2017,
author = {Ravi Srinivasan and Tamzeed Islam and Bashima Islam and Zeyu
Wang and Tamim Sookoor and Nirjon Shahriar and Omprakash Gnawali},
title = {{Preventive Maintenance of Centralized HVAC Systems: Use of
Acoustic Sensors, Feature Extraction, and Unsupervised Learning}},
booktitle = {Proceedings of the 15th biennial International Building
Performance Simulation Association Building Simulation Conference
(BuildingSimulation 2017)},
month = aug,
year = {2017}
}