| Issue |
Int. J. Metrol. Qual. Eng.
Volume 14, 2023
|
|
|---|---|---|
| Article Number | 10 | |
| Number of page(s) | 9 | |
| DOI | https://doi.org/10.1051/ijmqe/2023009 | |
| Published online | 18 July 2023 | |
Research article
Improved Bayesian network-based for fault diagnosis of air conditioner system
College of Quality and Safety Engineering, China Jiliang University, Hangzhou, PR China
* Corresponding author: qiangwang@cjlu.edu.cn
Received:
26
February
2023
Accepted:
15
June
2023
To solve the problem of fault prediction and diagnosis of household air conditioning, an improved Bayesian network (BN) fault diagnosis model is proposed. Firstly, the orthogonal defect classification (ODC) is used to make statistics and analysis of air conditioning fault data, and the structure of BN fault diagnosis model is built based on the analysis results. Then, genetic algorithm (GA) is used to optimize the conditional probability of network nodes and determine the network parameters. Finally, the cooling and heating failure data of household air conditioning were taken as an example to diagnose. Compared with the traditional BN model, the accuracy of fault diagnosis increases from 81.13% to 92.83%, which verifies the effectiveness of the model.
Key words: Household air conditioning systems / fault diagnosis / Bayesian network / orthogonal defect classification / genetic algorithm
© J. Xu et al., Published by EDP Sciences, 2023
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