A fault Diagnosis Method for Electroacoustic Signals Based on Support Vector Machine
The article focuses on the fault diagnosis problem of transformers in the field of power equipment status monitoring, with electroacoustic technology as the core and support vector machine method combined, proposing a new fault diagnosis scheme. Firstly, taking transformers as an example, the electrical equipment status monitoring system was studied. Secondly, an ensemble learning method is introduced to diagnose faults using support vector machine models. Finally, conduct simulation experiments on MATLAB. The experimental results show that this method can effectively identify normal and abnormal states, with high accuracy, precision, and recall.
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