Design and Implementation of Fault Diagnosis System for Power Internet of Things Equipment Based on Neural Network

Abstract

The design and application of the equipment fault diagnosis system have been improved and upgraded, allowing it to effectively detect the equipment’s operation status and promptly eliminate hidden faults, reducing the occurrence of unexpected accidents and improving the safety index of people’s lives. The purpose of this essay is to design and apply neural network (NN) fault diagnosis system model in power Internet of things (IOT) equipment and explore its accuracy and effectiveness. The BP neural network (BPNN) algorithm was used to construct model of a fault monitoring testing of the power IOT equipment. Neural network is an algorithmic mathematical model that imitates the behavioral characteristics of animal neural network and performs distributed parallel information processing. The network parameters were as follows: there were four input layer nodes, seven hidden layer nodes, and five output layer nodes, the training times were 10000, and the allowable error was 0.002. In this paper, we use the IOT to detect model of a fault monitoring testing of power equipment designed in each sample, the success rate is as high as 97.5%, and the designed network structure and network parameters are reasonable. The trained loss is less than 0.001, and the nontraining set samples may be appropriately identified. It is clear that the NN has a high application for power equipment fault diagnosis in the IOT value.

Research on neural network-based fault diagnosis and prediction method for power communication equipment

“…Since BiGRU cannot fully consider the dependencies between faulty entity labels. Therefore, this chapter adopts CRF to fully consider the connected relationships between faulty entities so as to obtain the global optimal sequence and improve the accuracy of entity recognition [25][26]. Specifically, given 12 ( , , )…”