Yusri, Muhammad Yuszairie and Ngatiman, Nor Azazi and Shamsudin, Shamsul Anuar and Othman, Muhammad Nur (2024) Wireless HVAC compressor diagnostics using state of the art machine learning-based signal analysis Z-freq 2D. In: 7th International Conference on Mechanical Engineering Research 2023, ICMER 2023, 12 September 2023 through 13 September 2023, Kuantan.
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Wireless HVAC compressor diagnostics using state of the art machine learning-based signal analysis Z-freq 2D.pdf Restricted to Registered users only Download (2MB) |
Abstract
One of the priorities in detecting a faulty car engine is through a method known as diagnostic, and it is very crucial as each diagnostic able to provide information and assessment to identify problems in the car A/C compressor. Early detection of a compressor malfunction, is a fast way to prevent any heavy maintenance of vehicles either in the short or long term. This paper introduced a new statistical method to find the faulty in the vehicle's A/C compressor which is known as Z-freq 2D. The foundation of Z-freq 2D is involving the implementation of a Z-notch frequency domain filter. This approach was enhanced by using a special sensor that can detect two axial axes known as the Phantom Vibration Sensor specifically designed to detect and monitor the performance of the A/C compressor of the vehicle. Using the sensor, the data were recorded at numerous parameter sets of compressor speed. The analyzed data shows that Z-freq 2D coefficient is increase as the speed of the compressor increase over the duration of time. Z-freq 2D can be used to detect the malfunction and the irregularities of vibration signals, which may be indicated that the compressor is failing.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | diagnostic, faulty, statiscal method, vibration, Z-freq 2D |
| Divisions: | Faculty Of Mechanical Technology And Engineering |
| Depositing User: | Norhairol Khalid |
| Date Deposited: | 05 Jun 2025 08:42 |
| Last Modified: | 05 Jun 2025 08:42 |
| URI: | http://eprints.utem.edu.my/id/eprint/28772 |
| Statistic Details: | View Download Statistic |
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