多传感器融合的船舶轮机设备多发故障信号监测研究  

Research on multi-sensor fusion multi-fault signal monitoring of Marine turbine equipment

作　　者：赵云博[1] ZHAO Yun-bo(Jiangsu Maritime Institute,Nanjing 211170,China)

机构地区：[1]江苏海事职业技术学院,江苏南京211170

出　　处：《舰船科学技术》2022年第17期114-117,共4页Ship Science and Technology

摘　　要：为提升多发故障信号监测效果,提出多传感器融合的船舶轮机设备多发故障信号监测方法。多个传感器采集船舶轮机设备运行信号,通过经验小波变换提取轮机设备多发故障信号特征;神经网络根据故障信号特征得到单个传感器的多发故障信号监测结果,将单个传感器的监测结果为基本概率分配函数,根据证据理论获取最终的船舶轮机设备多发故障信号监测结果。实验证明:该方法可有效采集船舶轮机设备运行信号,并提取多发故障信号特征;该方法可有效检测多发故障信号,具备较高的多发故障信号监测精度。To improve the monitoring effect of multiple fault signals, a multi-sensor fusion monitoring method for multiple fault signals of marine engine equipment is proposed. Multiple sensors collect the operation signals of marine engine equipment, and extract the characteristics of multiple fault signals of marine engine equipment through empirical wavelet transform. The neural network obtains the monitoring results of multiple fault signals of a single sensor according to the characteristics of fault signals. The monitoring results of a single sensor are regarded as the basic probability distribution function, and the final monitoring results of multiple fault signals of marine engine equipment are obtained according to the evidence theory. The experimental results show that this method can effectively collect the operation signals of marine engine equipment and extract the characteristics of multiple fault signals. This method can effectively detect multiple fault signals and has high monitoring accuracy.

关 键 词：多传感器融合 船舶轮机设备 多发故障 信号监测 小波变换 证据理论 

分 类 号：TP206.3[自动化与计算机技术—检测技术与自动化装置]