Application of Adaboost Algorithm on Automatic Detection of Electrical Equipment Operation Status

• Published in: Journal of physics. Conference series 2019-11, Vol.1345 (5), p.52049
• Main Authors: Xue, Zhicheng, Gao, Wenjiang, Li, Xuan, Weinan, Wu
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Analog circuits; Classification; Classifiers; Electric equipment; Fault diagnosis; Kernel functions; Monitoring; Physics; Support vector machines
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1345/5/052049

The algorithm can integrate weak classifiers that are slightly better than random guesses, and output strong classifiers with higher classification accuracy. In order to further improve the classification accuracy of the algorithm, an infinite dimensional algorithm based on support vector machine is established. The key to realize the infinite dimensional algorithm is to establish a new support vector machine kernel function, so that this kernel function integrates infinite multiple algorithm weak classifier. The infinite dimensional algorithm is used for analog circuit fault diagnosis. The fault diagnosis results show that the classification accuracy of the infinite dimensional algorithm is better than the finite dimensional algorithm, which improves the classification accuracy of the algorithm. Based on the analysis of the operation status and fault diagnosis of modern electrical equipment, this paper discusses the necessity and possibility of monitoring the operation status of electrical equipment, and takes the method of electrical operation monitoring and the method of fault diagnosis as the research object. The methods and fault diagnosis methods of current electrical equipment operation monitoring are expounded. On this basis, the future development trend of equipment operation monitoring is explained, and the importance of online monitoring development is analyzed.