基于BP神经网络的船用柴油机振动状态监测

通过监测柴油机表面振动信号,用时间序列分析方法提取柴油机故障的振动特征参数,以此建立相应的神经网络,用于船用柴油机的状态监测,提高诊断的准确性。试验研究在中速四冲程增压柴油机上进行。文中以柴油机气阀间隙异常的诊断和柴油机负荷状态的识别为例阐述了该方法的实现过程,并给出了振动信号的特征参数与柴油机工作状态之间的关系。研究表明,利用神经网络监测柴油机运行状态的变化是可行的和有效的。

Marine diesel engine vibration monitoring based on BP neural network

This paper conducts an investigation on marine diesel engine fault diagnosis based on vibration monitoring and artificial neural network.The engine surface vibration signals are measured and analyzed by using time series method.The characteristic parameters of engine vibration signals obtained from time series analysis are used to build a suitable artificial neural network using the Back Propagation Algorithm in order to detect the engine operating faults and improve the diagnosis accuracy.The diagnoses of variations in valve clearances and engine cylinder loads are discussed.The relationships between vibration characteristics and engine working conditions are presented.Experimental investigations were carried out on a medium speed four-stroke turbocharged diesel engine.The results show that the artificial neutral networks established for different purposes are feasible for diagnosing the abnormal valve clearances and detecting the cylinder loads of diesel engines,the diagnosis results are veracious and the accuracy is quite high.