Fault Feature Recognition Of Satellite Power System Based On Incremental Learning

In modern communications and technology, satellite power systems play a central role, and their reliability and fault diagnosis technology are increasingly important. Traditional fault diagnosis methods mainly include signal processing and model-based methods, but they are often inefficient and have limited accuracy when processing complex data sets. This paper proposes a feature selection algorithm based on incremental learning to improve the efficiency and accuracy of fault diagnosis. Its advantage is that it can only evaluate the newly added feature set without the need to reprocess the entire data set, thus optimizing the feature selection process and significantly improving the efficiency and response speed of the algorithm. In order to verify its effectiveness, this article selected 8 public data sets and 4 satellite power system simulation data sets for testing. Then, widely used algorithms are introduced to compare with the algorithm proposed in this article for a more comprehensive comparative analysis. By comparing the differences in running time and classification accuracy of the algorithms, it is concluded that the algorithm proposed in this article can significantly reduce the running time while maintaining or improving the classification effect, verifying the feasibility of the algorithm.