基于知识图谱的鼓风机轴承温度智能预测

Intelligent Prediction of Blower Bearing Temperature Based on Knowledge Graph

轴承温度是衡量鼓风机是否正常运行的重要指标之一.然而,轴承通常安装在狭小密闭的空间中,导致其温度难以实时准确检测.为了解决这个问题,设计了基于知识图谱的鼓风机轴承温度智能预测方法.利用统计方法分析鼓风机运行系统,获取与轴承温度相关的影响因素.结合运行机理和领域知识构建知识图谱,提取影响轴承温度的直接和间接特征变量.采用双模块模糊神经网络对知识图谱进行推理,实现对鼓风机轴温的实时准确预测.结果表明,基于知识图谱的鼓风机轴承温度智能预测方法可以准确地建模鼓风机系统,具有良好的温度预测能力.该项研究可以为轴承温度的实时监测和变化趋势预测提供支持.

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.