Intelligent Prediction of Blower Bearing Temperature Based on Knowledge Graph
The bearing temperature of the blower is an important indicator to evaluate its stable operation.However,since bearings are usually installed in a relatively closed environment,it is difficult to achieve real-time and accurate detection of bearing temperature.To address this issue,a knowledge graph-based intelligent prediction of the bearing temperature of blowers is presented.First,a statistical method is applied to analyze the operational system of blowers,and the influencing factors related to bearing temperature are obtained.Second,a knowledge graph is constructed by combining mechanism and domain knowledge.In addition,the direct and indirect feature variables that affect the bearing temperature are extracted.Third,a dual modular fuzzy neural network is designed to deduce the knowledge graph,and the real-time and accurate prediction of the bearing temperature of blowers is realized.Finally,the results show that the intelligent prediction method of bearing temperatures of blowers based on a knowledge graph can accurately model the blower system and has good temperature prediction ability.This research can provide support for real-time monitoring and change trend prediction of bearing temperatures.
万方数据
维普
