Equipment Fault Prediction Method in Power Communication Network Based on Equipment Frequency Domain Characteristics

There are a large number of communication operation and maintenance equipment in the power IoT scenario. It is difficult to find out when the equipment fails. The traditional method is mainly manual maintenance, but the efficiency is low. In this paper, a neural network-based equipment fault prediction method is proposed. By collecting the time series data of the equipment and transforming it into frequency domain features by using discrete Fourier transform, the neural network model is trained. The experiment shows that the proposed method avoids the complex timing characteristics of the equipment. The problem has improved the ability of equipment failure prediction.