Refrigerant Leakage Detection and Diagnosis for a Distributed Refrigeration System

Assawamartbunlue, Kriengkrai; Brandemuehl, Michael J.

doi:10.1080/10789669.2006.10391186

Cited by 6 publications

(6 citation statements)

References 5 publications

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“…AFDD methods for industrial systems typically function by monitoring specific performance indices or features that are sensitive to a particular fault. When a performance index significantly deviates from its expected value or exceeds a predetermined threshold, the AFDD method can detect the presence of a fault [15][16][17][18][19][20][21][22][23]. In other cases, it is addressed as a classification problem [24].…”

Section: Introductionmentioning

confidence: 99%

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

Wijethunga,

Nouraei,

Zych

et al. 2024

Energies

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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.

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Section: Introductionmentioning

confidence: 99%

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

Wijethunga,

Nouraei,

Zych

et al. 2024

Energies

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“…The fault detection performance of the SVDD method was better than the PCA method. In [8], refrigerant leaks were detected and diagnosed using a probabilistic ANN algorithm; the ANN could detect refrigerant losses with 90% accuracy. In [9], the better performance of a Probabilistic Neural Network is demonstrated compared to Back Propagation (BP) method as BP has random initial wights leading to a less reliable system.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of Supermarket Refrigeration Systems Using Convolutional Neural Network

Soltani

Soerensen²,

Leth

et al. 2020

IECON 2020 the 46th Annual Conference of the IEEE Industrial Electronics Society

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The functionality of supermarket refrigeration systems (SRS) has a significant impact on the quality of food products and potentially human health. Automatic fault detection and diagnosis of SRS is desired by manufacturers and customers as performance is improved, and energy consumption and cost is lowered. In this work, Convolutional Neural Networks (CNN) are applied for fault detection and diagnosis of SRS. The network is found to be able to classify the fault with 99% accuracy. The sensitivity of the designed model to data quality is also assessed. The results show that the model can classify faults at low sample rates if the training set is large enough. Moreover, the model displays low sensitivity to data quality such as noisy and perturbed validation data, and the frequency of false positives is satisfactorily low as well.

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“…A considerable portion of the charge is released to the atmosphere, due to leakage in the refrigeration piping, valves, and joints (Francis et al 2017). The annual leakage rate has been estimated in the range of 15%-20% by Assawamartbunlue and Brandemuehl (2006) to 25% by ICF Consulting (2005). Because of this leakage, supermarkets have significant direct and indirect emission rates in addition to the energy-related impacts (James and James 2010;Beshr et al 2015;Islam et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Supermarket system characteristics and operating faults (RP-1615)

Behfar

Yuill

2018

Science and Technology for the Built Environment

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Supermarkets undergo various types of operating faults that can require costly preventive maintenance, service, and repairs. Because supermarkets are growing in number and in the variety of system types and characteristics, information about common faults and equipment is essential prior to investing effort on the development of new fault detection and diagnostics (FDD) methods or other improvements to system design and operation. A study was conducted to investigate supermarket equipment characteristics, such as the prevalence of central vs. distributed systems, condenser types, control strategies, and common operating faults such as refrigerant leakage, failed evaporator or condenser fans, failed compressors, etc. The data come from four sources: expert surveys, facility management system messages, service calls, and service records. Some of the results of the study are that centralized direct expansion systems with on/off compressor capacity control strategy and air-cooled condensers are the most common system types and refrigerant charge problems are the most common source of equipment failure. Case-related problems, in aggregate, are the most frequently occurring faults in supermarkets.

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Refrigerant Leakage Detection and Diagnosis for a Distributed Refrigeration System

Cited by 6 publications

References 5 publications

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

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

Fault Detection of Supermarket Refrigeration Systems Using Convolutional Neural Network

Supermarket system characteristics and operating faults (RP-1615)

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