AUTHOR=Qian Hong , Pan Yutong , Wang Xuehua , Li Zhenpeng 

TITLE=Research on the optimization of belief rule bases using the Naive Bayes theory

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 12 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2024.1396841

DOI=10.3389/fenrg.2024.1396841

ISSN=2296-598X

ABSTRACT=The belief rule-based is crucial in expert systems for intelligent diagnosis of equipment. However, in the belief rule-based for fault diagnosis, multiple antecedent attributes in belief rule-based are often initially determined by domain experts. Actually, multiple fault symptoms related to multiple antecedent attributes are different when actual fault happening. This leads to results of multiple antecedent attributes matching with multiple fault symptoms non simultaneously, thereby result of fault diagnosis lack of timeliness and accuracy. To address this issue, this paper proposes a method for belief rule-based optimization based on Naive Bayes theory. Firstly, taking fault sample in a long enough window, dividing into several intervals samples, making the analysis samples approximate the overall samples. Secondly, using Gaussian Mixture Clustering and Naive Bayes optimization, Iterate over the threshold and limit values of fault symptoms in belief rule-based, based on the requirements of the timeliness and accuracy of fault diagnosis results. Finally, the belief rule-based is optimized. By using fault samples from high-pressure heaters and condensers, it is demonstrated that a significant improvement in the timeliness and accuracy of fault diagnosis with the optimal belief rule-based.