基于多代理决策融合的电机状态识别

提出了基于多代理决策融合的电机状态识别系统.以电机的振动信号和电流信号为输入,六种分类器用来识别其状态.每个分类器视为一个代理,独立完成模式识别工作后,同其他分类器交换信息从而提高识别率.本文还将传感器融合和分类器选择融入系统,同单源数据和无分类器选择相比具有更大的优势,使最终电机状态识别率达到98.9%.

Motor condition recognition based on multi-agent decision fusion

One motor condition recognition system based on multi-agent decision fusion was proposed.Six classifiers were used to classify motors condition by system inputs:vibration and current signals.In the system,each classifier was considered as an agent,which independently completed recognition task,then exchanged information among classifiers to improve classification accuracy.Sensor fusion and classifier selection were put into the system,and this method was much better than one-single signal and no classifier selection.The best recognition result of the proposed system achieved 98.9%.