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

Download files

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

  • 74
    total views
  • 126
    total downloads
  • 1
    views this month
  • 4
    downloads this month

Export as

Related outputs

Power Grid Frequency Forecasting from μPMU Data using Hybrid Vector-Output LSTM network
Dey, M., Wickramarachchi, D., Rana, S.P., Simmons, .C.V and Dudley, S. (2023). Power Grid Frequency Forecasting from μPMU Data using Hybrid Vector-Output LSTM network. 2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE). 23 - 26 Oct 2023 IEEE. https://doi.org/10.1109/ISGTEUROPE56780.2023.10408056
MammoWave Breast Imaging Device: Prospective Clinical Trial Results and AI Enhancement
Ghavami, N., Sánchez-Bayuela, D.A., Badia, M., Papini, L., Rana, S.P., Bigotti, A., Palomba, G., Raspa, G., Castellano, C.R., Bernardi, D., Tagliafico, A., Calabrese, M., Dudley, S., Ghavami, M. and Tiberi, G. (2023). MammoWave Breast Imaging Device: Prospective Clinical Trial Results and AI Enhancement. IEEE. https://doi.org/10.1109/cama57522.2023.10352699
Power Grid Frequency Forecasting from μPMU Data using Hybrid Vector-Output LSTM network
Dey, M., Wickramarachchi, D., Rana, S.P., Simmons, C.v. and Dudley, S. (2023). Power Grid Frequency Forecasting from μPMU Data using Hybrid Vector-Output LSTM network. 2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE). https://doi.org/10.1109/isgteurope56780.2023.10408056
Breast Cancer Detection using Machine Learning Approaches on Microwave-based Data
Ghavami, M., Ghavami, N., Rana, S. and Tiberi, G. (2023). Breast Cancer Detection using Machine Learning Approaches on Microwave-based Data. EUCAP 23. Florence, Italy 26 - 31 Mar 2023
Radiation-free Microwave Technology for Breast Lesion Detection using Supervised Machine Learning Model
Rana, S., Dey, M., Loretoni, R., Duranti, M., Ghavami, M., Dudley-Mcevoy, S. and Tiberi, G. (2023). Radiation-free Microwave Technology for Breast Lesion Detection using Supervised Machine Learning Model. Tomography. 9 (1), pp. 105-129. https://doi.org/10.3390/tomography9010010
High-resolution electrical measurement data processing
Dey, M. and Rana, S. (2022). High-resolution electrical measurement data processing. GB2599698
Detecting Power Grid Frequency Events from μPMU Voltage Phasor Data Using Machine Learning
Dey, M., Rana, S., Wylie, J., Simmons, C. V. and Dudley-Mcevoy, S. (2022). Detecting Power Grid Frequency Events from μPMU Voltage Phasor Data Using Machine Learning. The IET 11th International Conference on Renewable Power Generation. IET London: Savoy Place. 22 - 23 Sep 2022 Institute of Engineering and Technology (IET).
Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network
Dey, M., Rana, S., Loretoni, R., Duranti, M., Sani, L., Vispa, A., Raspa, G., Ghavami, M., Dudley-Mcevoy, S. and Tiberi, G. (2022). Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network. PLoS ONE. https://doi.org/10.1371/journal.pone.0271377
Markerless Gait Classification Employing 3D IR-UWB Physiological Motion Sensing
Rana, S., Dey, M., Ghavami, M. and Dudley-Mcevoy, S. (2022). Markerless Gait Classification Employing 3D IR-UWB Physiological Motion Sensing. IEEE Sensors Journal. 22 (7), pp. 6931-6941. https://doi.org/10.1109/JSEN.2022.3154092
Radial Basis Function for Breast Lesion Detection from MammoWave Clinical Data
Rana, S., Dey, M., Riccardo Loretoni, Michele Duranti, Lorenzo Sani, Alessandro Vispa, Ghavami, M., Sandra Dudley and Gianluigi Tiberi (2021). Radial Basis Function for Breast Lesion Detection from MammoWave Clinical Data. Diagnostics. 11 (10). https://doi.org/10.3390/diagnostics11101930
Solar farm voltage anomaly detection using high-resolution μ PMU data-driven unsupervised machine learning
Dey, M., Rana, S., Simmons, Clarke V. and Dudley-Mcevoy, S. (2021). Solar farm voltage anomaly detection using high-resolution μ PMU data-driven unsupervised machine learning. Applied Energy. 303, p. 117656. https://doi.org/10.1016/j.apenergy.2021.117656
Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network
Dey, M., Rana, S., Loretoni, R., Duranti, M., Sani, L., Vispa, A., Raspa, G., Ghavami, M., Dudley-Mcevoy, S. and Tiberi, G. (2021). Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network. London South Bank University. https://doi.org/10.18744/lsbu.8xz49
Automated terminal unit performance analysis employing x-RBF neural network and associated energy optimisation – A case study based approach
Dey, M., Rana, S. and Dudley-Mcevoy, S. (2021). Automated terminal unit performance analysis employing x-RBF neural network and associated energy optimisation – A case study based approach. Applied Energy. 298, p. 117103. https://doi.org/10.1016/j.apenergy.2021.117103
3D Gait Abnormality Detection Employing Contactless IR-UWB Sensing Phenomenon
Rana, S., Dey, M., Ghavami, M. and Dudley-McEvoy, S. (2021). 3D Gait Abnormality Detection Employing Contactless IR-UWB Sensing Phenomenon. IEEE Transactions on Instrumentation and Measurement. 70. https://doi.org/10.1109/TIM.2021.3069044
Data-driven remote fault detection and diagnosis of HVAC terminal units using machine learning techniques
Dey, M. (2020). Data-driven remote fault detection and diagnosis of HVAC terminal units using machine learning techniques. PhD Thesis London South Bank University School of Engineering https://doi.org/10.18744/lsbu.9499w
Non-Intrusive Gait Recognition Employing Ultra Wideband Signal Detection
Rana, S. (2020). Non-Intrusive Gait Recognition Employing Ultra Wideband Signal Detection. PhD Thesis London South Bank University School of Engineering https://doi.org/10.18744/lsbu.94988
A Case Study Based Approach for Remote Fault Detection Using Multi-Level Machine Learning in A Smart Building
Dey, M, Rana, SP and Dudley, S (2020). A Case Study Based Approach for Remote Fault Detection Using Multi-Level Machine Learning in A Smart Building. Smart Cities. 3 (2), pp. 401-419. https://doi.org/10.3390/smartcities3020021
A robust FLIR target detection employing an auto-convergent pulse coupled neural network
Dey, M., Rana, S.P. and Siarry, P. (2019). A robust FLIR target detection employing an auto-convergent pulse coupled neural network. Remote Sensing Letters. 10 (7), pp. 639-648. https://doi.org/10.1080/2150704x.2019.1597296
Boosting content based image retrieval performance through integration of parametric & nonparametric approaches
Rana, S., Dey, M. and Siarry, P. (2019). Boosting content based image retrieval performance through integration of parametric & nonparametric approaches. Journal of Visual Communication and Image Representation. 58, pp. 25-219. https://doi.org/10.1016/j.jvcir.2018.11.015
Signature Inspired Home Environments Monitoring System Using IR-UWB Technology
Rana, S., Dey, M., Ghavami, M. and Dudley-Mcevoy, S. (2019). Signature Inspired Home Environments Monitoring System Using IR-UWB Technology. Sensors. 19 (2), p. 385. https://doi.org/10.3390/s19020385
Non-Contact Human Gait Identification through IR-UWB Edge Based Monitoring Sensor
Rana, S., Dey, M, Ghavami, M and Dudley-McEvoy, S (2019). Non-Contact Human Gait Identification through IR-UWB Edge Based Monitoring Sensor. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2019.2926238
Machine Learning Approaches for Automated Lesion Detection in Microwave Breast Imaging Clinical Data
Rana, S., Dey, M., Tiberi, G., Sani, L., Vispa, A., Raspa, G., Duranti, M., Ghavami, M. and Dudley-Mcevoy, S. (2019). Machine Learning Approaches for Automated Lesion Detection in Microwave Breast Imaging Clinical Data. Scientific Reports. 9, p. 10510. https://doi.org/10.1038/s41598-019-46974-3
ITERATOR: A 3D Gait Identification from IR-UWB Technology
Rana, S., Dey, M, Ghavami, M and Dudley, S (2019). ITERATOR: A 3D Gait Identification from IR-UWB Technology. International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (EMBC 2019). Berlin, Germany 23 - 27 Jul 2019
Biometric Security and Internet of Things (IoT)
Obaidat, M. S., Rana, S., Maitra, T., Giri, D. and Dutta, S. (2018). Biometric Security and Internet of Things (IoT). in: Biometric-Based Physical and Cybersecurity Systems Springer. pp. 477–509
Smart Building Creation in Large Scale HVAC Environments through Automated Fault Detection and Diagnosis
Dudley, S, Dey, M and Rana, S. (2018). Smart Building Creation in Large Scale HVAC Environments through Automated Fault Detection and Diagnosis. Future Generation Computer Systems. 108, pp. 950-966. https://doi.org/10.1016/j.future.2018.02.019
Remote Vital Sign Recognition Through Machine Learning Augmented UWB
Dudley, S, Rana, S., Dey, M, Brown, R and Siddiqui, H (2018). Remote Vital Sign Recognition Through Machine Learning Augmented UWB. European Conference on Antennas and Propagation. Excel London, Docklands 09 - 13 Apr 2018 London South Bank University. https://doi.org/10.1049/cp.2018.0978
A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building
Rana, S., Dey, M and Dudley, S (2018). A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building. Sensors. 18 (11), pp. 1-15. https://doi.org/10.3390/s18113766
Semi-Supervised Learning Techniques for Automated Fault Detection and Diagnosis of HVAC System
Dudley, S, Dey, M and Rana, S. (2018). Semi-Supervised Learning Techniques for Automated Fault Detection and Diagnosis of HVAC System. IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2018). Volos, Greece 05 - 07 Nov 2018
A PID inspired feature extraction method for HVAC terminal units
Dey, M., Gupta, M., Rana, S., Turkey, M. and Dudley-Mcevoy, S. (2017). A PID inspired feature extraction method for HVAC terminal units. IEEE Conference on Technologies for Sustainability (SusTech 2017). Phoenix, Arizona, USA 12 - 14 Nov 2017 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/sustech.2017.8333470
Unsupervised Learning Techniques for HVAC Terminal Unit Behaviour Analysis
Dey, M, Gupta, M, Turkey, M and Dudley, S (2017). Unsupervised Learning Techniques for HVAC Terminal Unit Behaviour Analysis. IEEE International Conference on Smart City Innovations. Fremont, California, USA 04 - 08 Aug 2017 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/UIC-ATC.2017.8397584
UWB Localization Employing Supervised Learning Method
Rana, S., Dey, M., Siddiqui, H., Tiberi, G., Ghavami, M. and Dudley, S (2017). UWB Localization Employing Supervised Learning Method. IEEE International Conference on Ubiquitous Wireless Broadband 2017. Salamanca, Spain 12 - 15 Sep 2017 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICUWB.2017.8250971
A PID Inspired Feature Extraction for HVAC Terminal Units
Dey, M, Gupta, M, Rana, S., Turkey, M and Dudley, S (2017). A PID Inspired Feature Extraction for HVAC Terminal Units. IEEE Conference on Technologies for Sustainability (SusTech 2017). Phoenix, Arizona, USA 12 - 14 Nov 2017 Institute of Electrical and Electronics Engineers (IEEE).