Quantitative assessment of refrigerant impact on vehicle air-conditioning systems via Z-Freq 2D statistical analysis

Yusri, Muhammad Yuszairie and Ngatiman, Nor Azazi and Shamsudin, Shamsul Anuar and Othman, Muhammad Nur and Parnon, Mohamad Afiq Amiruddin (2024) Quantitative assessment of refrigerant impact on vehicle air-conditioning systems via Z-Freq 2D statistical analysis. ARPN Journal Of Engineering And Applied Sciences, 19 (17). pp. 1125-1133. ISSN 1819-6608

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Abstract

In this study, the impact of refrigerant types on the performance and efficiency of vehicle air-conditioning (AC) systems was quantitatively assessed using a novel two-dimensional Z-Freq 2D statistical analysis method. Wireless vibration accelerometers, capturing both horizontal and vertical vibrations, were utilized to measure the dynamic response of the air-conditioning compressor. Before data collection, meticulous calibration of sensors was conducted to ensure the accuracy and reliability of measurements. The introduction of the Z-Freq 2D statistical analysis technique, developed specifically for this research, allowed for a comprehensive examination of the vibrational data, facilitating a deeper understanding of the effects of different refrigerants on AC system performance. To validate the effectiveness and reliability of the Z-Freq 2D analysis, machine learning techniques were employed. These techniques provided a robust framework for the analysis of the statistical data, with performance evaluation indicators demonstrating the efficacy of the newly developed method. The experimental setup was based on an actual vehicle air-conditioning test rig, designed to simulate real-world operating conditions accurately. The findings of this research offer significant insights into the selection of refrigerants for vehicle AC systems, highlighting the potential for enhanced system performance and efficiency through the application of advanced statistical analysis and machine learning validation techniques. This study not only contributes to the field of automotive thermal management but also paves the way for future research in the optimization of vehicle AC systems.

Item Type:	Article
Uncontrolled Keywords:	Fault diagnostics, Statistical methods, Z-Freq 2D, Vibration monitoring, Machine learning.
Divisions:	Faculty Of Mechanical Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	11 Aug 2025 04:55
Last Modified:	11 Aug 2025 04:55
URI:	http://eprints.utem.edu.my/id/eprint/28900
Statistic Details:	View Download Statistic

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