Fault Diagnosis Model of GIS Equipment Based on Information Fusion Technology

Traditional fault diagnosis methods are often difficult to diagnose accurately and efficiently because of the complex relationships such as multiple faults, multiple representations and multiple correlations that may occur during the operation of GIS equipment. Therefore, this paper puts forward a GIS equipment fault diagnosis model based on information fusion technology, and realizes the accurate diagnosis of GIS equipment faults through multi-level and multi-source information fusion. Specifically, the model firstly collects all kinds of information in the process of equipment operation, such as temperature, pressure, vibration, etc., and then uses Bayesian network technology to fuse this information to establish the relationship model between faults and representative state variables. Through network learning and reasoning, the accurate diagnosis of equipment faults can be realized. In addition, the model also adopts the uncertainty processing method to deal with the uncertainty in the model reasonably, which improves the robustness and reliability of the model. A series of quantitative data were collected during the evaluation of GIS equipment fault diagnosis models based on information fusion technology. On the test set, my model achieved an average accuracy of 92%, with the highest accuracy reaching 95%. In complex and multi-fault scenarios, the accuracy of the model remains stable at over 88%. The application of this model can not only improve the operation efficiency and safety of GIS equipment, but also provide useful reference and enlightenment for fault diagnosis of other complex equipment.