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A data-driven approach for fault diagnosis in multi-zone HVAC systems: Deep neural bilinear Koopman parity

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Version 2 2023-10-09, 07:12
Version 1 2023-06-22, 04:20
journal contribution
revised on 2023-10-09, 07:11 and posted on 2023-10-09, 07:12 authored by Fatemeh Negar Irani, Mohammadhosein Bakhtiaridoust, Meysam Yadegar, Nader Meskin

Sensor faults in heating, ventilation, and air conditioning (HVAC) systems are inevitable and result in significant energy waste. This paper presents an innovative data-driven approach for sensor fault detection and isolation in multi-zone HVAC systems. The proposed solution integrates bilinear Koopman model realization, deep learning, and bilinear parity-space. A deep neural network realizes a bilinear model, enabling bilinear parity-space sensor fault detection and isolation. This yields a reliable, accurate, and interpretable data-driven framework. The method requires no prior HVAC dynamics knowledge, relying solely on normal operation data. It diagnoses additive, multiplicative, and complete failure sensor faults while minimizing false alarms, even with severe faults. A four-zone HVAC system is simulated in TRNSYS as a case study to demonstrate the performance and efficacy of the proposed approach. The proposed bilinear deep Koopman model realization is utilized to develop a bilinear model for the four-zone HVAC system. The bilinear model is then used for designing the bilinear parity-space. Further, considering various failure scenarios, the proposed sensor fault detection and isolation framework demonstrates promising diagnosis performance. Finally, a comparison is conducted to showcase the advantages of the proposed method over earlier works based on PCA and neural networks.

Other Information

Published in: Journal of Building Engineering
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: http://dx.doi.org/10.1016/j.jobe.2023.107127

Funding

Open Access funding provided by the Qatar National Library

History

Language

  • English

Publisher

Elsevier

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License

Institution affiliated with

  • Qatar University
  • College of Engineering - QU