作　　者： ; ; ;

机构地区： 广东交通职业技术学院

出　　处： 《船电技术》 2012年第6期9-11,共3页

摘　　要： 为了解决船用柴油机故障诊断中基于单传感器信息的方法诊断精度低的缺点,应用神经网络原理,提出了一种基于气缸压力,缸盖振动信号和燃油压力等多传感器信息融合的喷油器故障诊断新方法。通过提取船用柴油机工作过程故障三种信号的八个特征值,按正常和五种故障状态的构造学习样本文集和检验样本文集,对输入进行归一化处理,该方法能有效地提高其故障诊断精度。 In order to solve the non-linear and uncertain faults of engine, a new in-cylinder engine faults diagnosis method based on multi-sensor information fusion is presented by using the theory of neural network. Through extracting eight features of three faults signals in the operating process of the marine diesel engine, three kinds of signals are sampled and analyzed using the pressure in cylinder, liberation of cover and the fuel pressure packet methods. Through this method, the fault diagnosis accuracy is improved effectively.

关 键 词： 船用柴油机 故障 信息 融合

领　　域： [自动化与计算机技术] [自动化与计算机技术]