Power Equipment Fault Diagnosis and Prediction Based on Improved ID3 Algorithm and Apriori Association Rule Algorithm

Abstract

Power equipment fault diagnosis is an important part of fault management work. Using data mining technology to diagnose equipment faults provides maintenance decision-making plans for equipment maintenance, improves power equipment fault management level, and reduces economic losses of power companies. Therefore, this paper studies the fault diagnosis of power equipment and analyzes the application of data mining algorithm in fault diagnosis and prediction. In this paper, a fault diagnosis expert system is designed to accurately diagnose equipment faults. In order to improve the accuracy of fault diagnosis, the ID3 algorithm is optimized and improved. The experimental research on the accuracy of fault diagnosis also shows that the improved ID3 algorithm has higher diagnostic accuracy than the unimproved algorithm, and the Apriori association rule algorithm can detect the power equipment components in operation. weights, mining association rules between components.