Simulation of Predictive Maintenance Model of Power Enterprise Equipment based on Deep Learning Algorithm

With the increasing complexity of the power system, higher requirements have been put forward for the stable and efficient operation of equipment. Predictive maintenance, as a strategy aimed at identifying and correcting potential faults in advance, provides an effective means to ensure the stable operation of power equipment. This study delves into the application and effectiveness of DL (Deep Learning) technology in predictive maintenance of equipment in power enterprises. The article first outlines the advantages and potential of DL in predictive maintenance, especially its unique advantages in handling large-scale, high-dimensional, and nonlinear data. Subsequently, we discussed in detail the key steps of data collection, preprocessing, and feature extraction, emphasizing the DL model’s ability to automatically extract features. In the process of model design and validation, this article explores various DL algorithms and their practical application effects in predictive maintenance of power equipment. Finally, through simulation verification, the DL model shows higher prediction accuracy compared to traditional methods.