<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The refrigeration unit is a core component of the air conditioner, and its typical faults are pipe scaling and blockage, which can cause refrigerant leakage in serious cases, and these faults can affect the cooling effect of the air conditioner and even affect the safety of using the air conditioner in serious cases. Therefore, it is necessary to study the fault diagnosis system of the refrigeration unit. The traditional fault diagnosis system for refrigeration units, which generally uses wireless sensor networks to transmit data, is a challenge because of its slow diagnosis speed and the low accuracy rate of fault diagnosis for refrigeration units. This paper uses a probabilistic neural network (PNN) to debug a blockage fault diagnosis algorithm system for simulated fault training. The system successfully achieves four levels of pre-warning for blockage faults: “uncertainty, micro-blockage, blockage, and severe blockage”, with the fastest pre-warning time of “uncertainty, micro-blockage” being less than 2 seconds. The experimental results of this paper show that the PNN system can take timely measures against blockage faults, and the fault diagnosis accuracy of the model reaches 97.97%, which is 13.34% higher than the traditional fault identification algorithm. The PNN model can pinpoint the blockage faults of the refrigeration unit, which enables the working state of the refrigeration unit to be continuously optimized, and the air conditioner to maintain energy-efficient operation. The fault simulation of the air conditioning refrigeration unit in automatic control mode verifies the blockage fault diagnosis’s accuracy and the algorithm’s feasibility, providing reference and learning for the development of fault diagnosis algorithms.</p>
</abstract>

The refrigeration unit is a core component of the air conditioner, and its typical faults are pipe scaling and blockage, which can cause refrigerant leakage in serious cases, and these faults can affect the cooling effect of the air conditioner and even affect the safety of using the air conditioner in serious cases. Therefore, it is necessary to study the fault diagnosis system of the refrigeration unit. The traditional fault diagnosis system for refrigeration units, which generally uses wireless sensor networks to transmit data, is a challenge because of its slow diagnosis speed and the low accuracy rate of fault diagnosis for refrigeration units. This paper uses a probabilistic neural network (PNN) to debug a blockage fault diagnosis algorithm system for simulated fault training. The system successfully achieves four levels of pre-warning for blockage faults: “uncertainty, micro-blockage, blockage, and severe blockage”, with the fastest pre-warning time of “uncertainty, micro-blockage” being less than 2 seconds. The experimental results of this paper show that the PNN system can take timely measures against blockage faults, and the fault diagnosis accuracy of the model reaches 97.97%, which is 13.34% higher than the traditional fault identification algorithm. The PNN model can pinpoint the blockage faults of the refrigeration unit, which enables the working state of the refrigeration unit to be continuously optimized, and the air conditioner to maintain energy-efficient operation. The fault simulation of the air conditioning refrigeration unit in automatic control mode verifies the blockage fault diagnosis’s accuracy and the algorithm’s feasibility, providing reference and learning for the development of fault diagnosis algorithms.

Probabilistic neural network-based diagnosis of blockage faults in air conditioning and refrigeration units

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The refrigeration unit is a core component of the air conditioner, and its typical faults are pipe scaling and blockage, which can cause refrigerant leakage in...

Probabilistic neural network-based diagnosis of blockage faults in air conditioning and refrigeration units

Online veröffentlicht: 11. Aug. 2023

Seitenbereich: -

Eingereicht: 17. Okt. 2022

Akzeptiert: 20. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00166

Schlüsselwörter
Refrigeration units, Fault diagnosis, Wireless sensors, Probabilistic neural networks, Fault simulation

© 2023 Yujie Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Probabilistic neural network-based diagnosis of blockage faults in air conditioning and refrigeration units

Online veröffentlicht: 11. Aug. 2023

Seitenbereich: -

Eingereicht: 17. Okt. 2022

Akzeptiert: 20. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00166

SchlüsselwörterRefrigeration units, Fault diagnosis, Wireless sensors, Probabilistic neural networks, Fault simulation

© 2023 Yujie Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Refrigeration units, Fault diagnosis, Wireless sensors, Probabilistic neural networks, Fault simulation