Author:
Xu Jiaqi,Wang Qiang,Zhou Juan,Zhou Haiting,Chen Jiayan
Abstract
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.
Funder
State Administration for Market Regulation of China
Zhejiang Special Support Program for High-Level Personnel Recruitment of China
Subject
Safety, Risk, Reliability and Quality
Cited by
2 articles.
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