Wireless HVAC compressor diagnostics using state of the art machine learning-based signal analysis Z-freq 2D
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 a...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2024
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| Online Access: | http://eprints.utem.edu.my/id/eprint/28772/1/Wireless%20HVAC%20compressor%20diagnostics%20using%20state%20of%20the%20art%20machine%20learning-based%20signal%20analysis%20Z-freq%202D.pdf http://eprints.utem.edu.my/id/eprint/28772/ https://iopscience.iop.org/issue/1742-6596/2688/1 |
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| Summary: | 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. |
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