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Precision Leak Detection in Supermarket Refrigeration Systems Integrating Categorical Gradient Boosting with Advanced Thresholding

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  • Rashinda Wijethunga

    (Department of Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada)

  • Hooman Nouraei

    (Neelands Group Ltd., Burlington, ON L7M 0V9, Canada)

  • Craig Zych

    (Neelands Group Ltd., Burlington, ON L7M 0V9, Canada)

  • Jagath Samarabandu

    (Department of Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada)

  • Ayan Sadhu

    (Department of Civil and Environmental Engineering, Western University, London, ON N6A 3K7, Canada)

Abstract

Supermarket refrigeration systems are integral to food security and the global economy. Their massive scale, characterized by numerous evaporators, remote condensers, miles of intricate piping, and high working pressure, frequently leads to problematic leaks. Such leaks can have severe consequences, impacting not only the profits of the supermarkets, but also the environment. With the advent of Industry 4.0 and machine learning techniques, data-driven automatic fault detection and diagnosis methods are becoming increasingly popular in managing supermarket refrigeration systems. This paper presents a novel leak-detection framework, explicitly designed for supermarket refrigeration systems. This framework is capable of identifying both slow and catastrophic leaks, each exhibiting unique behaviours. A noteworthy feature of the proposed solution is its independence from the refrigerant level in the receiver, which is a common dependency in many existing solutions for leak detection. Instead, it focuses on parameters that are universally present in supermarket refrigeration systems. The approach utilizes the categorical gradient boosting regression model and a thresholding algorithm, focusing on features that are sensitive to leaks as target features. These include the coefficient of performance, subcooling temperature, superheat temperature, mass flow rate, compression ratio, and energy consumption. In the case of slow leaks, only the coefficient of performance shows a response. However, for catastrophic leaks, all parameters except energy consumption demonstrate responses. This method detects slow leaks with an average F1 score of 0.92 within five days of occurrence. The catastrophic leak detection yields F1 scores of 0.7200 for the coefficient of performance, 1.0000 for the subcooling temperature, 0.4118 for the superheat temperature, 0.6957 for the mass flow rate, and 0.8824 for the compression ratio, respectively.

Suggested Citation

  • Rashinda Wijethunga & Hooman Nouraei & Craig Zych & Jagath Samarabandu & Ayan Sadhu, 2024. "Precision Leak Detection in Supermarket Refrigeration Systems Integrating Categorical Gradient Boosting with Advanced Thresholding," Energies, MDPI, vol. 17(3), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:736-:d:1333063
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    References listed on IDEAS

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    1. Jabeur, Sami Ben & Gharib, Cheima & Mefteh-Wali, Salma & Arfi, Wissal Ben, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
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