CuES: Conditional Uncorrelation-based Characteristic Enhancement and Fusion of Electrical Signals

The lifespan of a generator greatly depends on the quality and aging of its stator bar insulation material. Aging of insulation materials can lead to premature equipment failure and significant material loss, resulting in substantial economic losses. However, existing methods for predicting the lifespan of electronic wire bars have several drawbacks, such as slow training speed, the need for a large amount of training data, and a tendency to overfit. To address this issue, we propose a characteristic enhancement algorithm based on conditional uncorrelation. This algorithm leverages characteristic enhancement to generate an extensive dataset and utilizes subset selection to identify relevant electrical parameters for predicting the remaining life span of the stator bar's main insulation configurations. Experimental results demonstrate the advantages of our research compared to deep learning models. Our approach offers a promising solution for accurately predicting the remaining life of stator bar insulation, thereby facilitating effective maintenance planning and minimizing economic losses.