Research on the construction and application of electrical fault classification system based on Bayesian algorithm
1
Xuchang Vocational and Technical College, College of Mechatronics and Engineering. No. 4336, Xinxing East Road, Dongcheng District, Xuchang City, Henan Province, China
2
Xuchang Vocational Technical College, College of Mechatronics and Automotive Engineering. No. 4336, Xinxing East Road, Dongcheng District, Xuchang City, Henan Province, China
Submission date: 2025-01-14
Final revision date: 2025-05-22
Acceptance date: 2025-05-29
Online publication date: 2025-06-03
Publication date: 2025-06-03
Corresponding author
Xiaowei Zhang
Xuchang Vocational and Technical College, College of Mechatronics and Engineering. No. 4336, Xinxing East Road, Dongcheng District, Xuchang City, Henan Province, China.
Xuchang Vocational and Technical College, College of Mechatronics and Engineering. No. 4336, Xinxing East Road, Dongcheng District, Xuchang City, Henan Province, China.
Diagnostyka 2025;26(2):2025213
KEYWORDS
TOPICS
ABSTRACT
With the rapid development of smart building technology, the safe and stable operation of the electrical system has become the core demand of modern building management. This study aims to construct an automated classification system for electrical faults based on Bayesian algorithm to improve the accuracy and efficiency of fault diagnosis. First, the wavelet transform is utilized for noise reduction and feature extraction of electrical signals to enhance the signal-to-noise ratio of the data. Subsequently, multi-category fault diagnosis is realized based on association vector machine, and Bayesian approach is combined to quantify the uncertainty factors and improve the classification reliability. The results show that the system performs well with small sample data, and the average recognition accuracy of various types of faults exceeds 70%. The wavelet transform-based fault recognition method demonstrates high stability, with the highest accuracy reaching 100% and the lowest still maintaining around 90%. In addition, the Bayesian classifier significantly improves the confidence level of fault diagnosis after parameter optimization, which verifies the effectiveness of the algorithm. It provides a feasible solution for the fault prediction and health management of power systems in intelligent buildings.
FUNDING
This paper was supported by Key Research Projects of Higher Education Institutions in Henan. Province Research and Application of Key Technology of Intelligent Protection Measurement and Control Terminal for Domestic Multicore Heterogeneous New Energy Box Transformer under grant No. 25B470016.
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