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Health Prognostics Classification with Autoencoders for Predictive Maintenance of HVAC Systems

Author

Listed:
  • Ruiqi Tian

    (Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada)

  • Santiago Gomez-Rosero

    (Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada)

  • Miriam A. M. Capretz

    (Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada)

Abstract

Buildings’ heating, ventilation, and air-conditioning (HVAC) systems account for significant global energy use. Proper maintenance can minimize their environmental footprint and enhance the quality of the indoor environment. The adoption of Internet of Things (IoT) sensors integrated into HVAC systems has paved the way for predictive maintenance (PdM) grounded in real-time operational metrics. However, HVAC systems without such sensors cannot leverage the advantages of current data-driven PdM techniques. This work introduces a novel data-driven framework, the health prognostics classification with autoencoders (HPC-AE), designed specifically for PdM. It utilizes solely HVAC power consumption and outside temperature readings for its operations, both of which are readily obtainable. The primary objective of the HPC-AE framework is to facilitate PdM through a health prognostic approach. The HPC-AE framework utilizes an autoencoder for feature enrichment and then applies an artificial neural network to classify the daily health condition of an HVAC system. A multi-objective evaluation metric is employed to ensure optimal performance of the autoencoder within this framework. This metric evaluates the autoencoder’s proficiency in reducing reconstruction discrepancies in standard data conditions and its capability to differentiate between standard and degraded data scenarios. The HPC-AE framework is validated in two HVAC fault scenarios, including a clogged air filter and air duct leakage. The experimental results show that compared to methods used in similar studies, HPC-AE exhibits a 5.7% and 2.1% increase in the F1 score for the clogged air filter and duct leakage scenarios.

Suggested Citation

  • Ruiqi Tian & Santiago Gomez-Rosero & Miriam A. M. Capretz, 2023. "Health Prognostics Classification with Autoencoders for Predictive Maintenance of HVAC Systems," Energies, MDPI, vol. 16(20), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7094-:d:1259782
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    References listed on IDEAS

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    1. Peng, Yuzhen & Rysanek, Adam & Nagy, Zoltán & Schlüter, Arno, 2018. "Using machine learning techniques for occupancy-prediction-based cooling control in office buildings," Applied Energy, Elsevier, vol. 211(C), pages 1343-1358.
    2. Arnaldo Rabello de Aguiar Vallim Filho & Daniel Farina Moraes & Marco Vinicius Bhering de Aguiar Vallim & Leilton Santos da Silva & Leandro Augusto da Silva, 2022. "A Machine Learning Modeling Framework for Predictive Maintenance Based on Equipment Load Cycle: An Application in a Real World Case," Energies, MDPI, vol. 15(10), pages 1-41, May.
    3. Emil Cazacu & Lucian-Gabriel Petrescu & Valentin Ioniță, 2022. "Smart Predictive Maintenance Device for Critical In-Service Motors," Energies, MDPI, vol. 15(12), pages 1-16, June.
    4. Thitiphat Klinsuwan & Wachiraphong Ratiphaphongthon & Rabian Wangkeeree & Rattanaporn Wangkeeree & Chatchai Sirisamphanwong, 2023. "Evaluation of Machine Learning Algorithms for Supervised Anomaly Detection and Comparison between Static and Dynamic Thresholds in Photovoltaic Systems," Energies, MDPI, vol. 16(4), pages 1-22, February.
    5. Alexandre Martins & Balduíno Mateus & Inácio Fonseca & José Torres Farinha & João Rodrigues & Mateus Mendes & António Marques Cardoso, 2023. "Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models," Energies, MDPI, vol. 16(6), pages 1-26, March.
    6. Antonio Gálvez & Alberto Diez-Olivan & Dammika Seneviratne & Diego Galar, 2021. "Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach," Sustainability, MDPI, vol. 13(12), pages 1-18, June.
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