基于数据融合的舰用柴油机状态预测系统研究  

Research on marine diesel engine's state prediction system based on data fusion

作　　者：张明书[1] 谢明江[2] 

机构地区：[1]武警工程学院,陕西西安710086 [2]西安交通大学机电信息研究所,陕西西安710056

出　　处：《中国修船》2007年第5期45-47,共3页China Shiprepair

摘　　要：融合柴油机热工、油液及振动3大类信息,采用灰色理论、自适应谐振理论(ART)和BP网络等相结合的决策级融合技术,建立了一套具有数据融合、分析诊断和状态预测能力的舰用柴油机状态预测系统,并以12PA6V-280型柴油机为研究对象,进行了工作过程仿真及故障模拟研究。With decision-making level fusion technology, integrating the grey theory, auto-adapted resonant theory （ART） and the BP network, the marine diesel engine＇s state prediction system is established to have the data fusion, analysis, diagnosis and condition forecast ability. With three main fusion data of diesel engine-thermotechnician, oil and vibration, the research based on the 12PA6V-280 diesel engine, is also carried on its working process and fault simulation.

关 键 词：柴油机 数据融合 神经网络 故障诊断 

分 类 号：U673[交通运输工程—船舶及航道工程]