Semi-supervised learning techniques for automated fault detection and diagnosis of HVAC systems

Conference paper

Dey, M., Rana, S. and Dudley, S. (2018). Semi-supervised learning techniques for automated fault detection and diagnosis of HVAC systems. IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2018). Volos, Greece 05 - 07 Nov 2018 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ictai.2018.00136
AuthorsDey, M., Rana, S. and Dudley, S.
TypeConference paper
Abstract

This work demonstrates and evaluates semisupervised learning (SSL) techniques for heating, ventilation and air-conditioning (HVAC) data from a real building to automatically discover and identify faults. Real HVAC sensor data is unfortunately usually unstructured and unlabelled, thus, to ensure better performance of automated methods promoting machine-learning techniques requires raw data to be preprocessed, increasing the overall operational costs of the system employed and makes real time application difficult. Due to the data complexity and limited availability of labelled information, semi-supervised learning based robust automatic fault detection and diagnosis (AFDD) tool has been proposed here. Further, this method has been tested and compared for more than 50 thousand TUs. Established statistical performance metrics and paired t-test have been applied to validate the proposed work.

Year2018
JournalProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Digital Object Identifier (DOI)https://doi.org/10.1109/ictai.2018.00136
Web address (URL)http://www.scopus.com/inward/record.url?eid=2-s2.0-85060788016&partnerID=MN8TOARS
Accepted author manuscript
License
File Access Level
Open
Publication dates
Online16 Dec 2018
Publication process dates
Deposited28 Jul 2022
Permalink -

https://openresearch.lsbu.ac.uk/item/9176v

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Accepted author manuscript
ICTAI-2018-CRC-172.pdf
License: CC BY 4.0
File access level: Open

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