摘要
排气温度是反映柴油机工作性能的重要参数。通常排气温度是采用热电偶等传感器进行数据监测,一旦传感器失效,就很难准确得到各缸排气温度实际测量值。本文针对柴油机热工参数之间复杂的非线性关系,提出了采用RBF神经网络技术融合柴油机热工参数数据进行智能检测的方法,以此对柴油机排气温度进行状态监测,达到了较高的检测精度。计算机仿真与实际应用表明,利用神经网络的智能检测技术预知柴油机的热工参数,对柴油机进行监测是切实可行的。
The exhaust temperature is an important parameter that reflects the working performance of the diesel engine, and it is monitored with the thermocouple sensors usually. Once the sensor invalidation occurs, it will be very difficult to measure the exhaust temperatures of each cylinder accurately. This paper takes into account the complicated non-linear relation of the thermal cycling parameters and brings forward an intelligent detection method of diesel engine exhaust temperature, which adopts the technique of RBF NN and takes the data of the thermal cycling parameters into consideration： Computer simulation and practical application prove that it is feasible to monitor the thermal cycling parameters of diesel engine with intelligent detection technique using NN.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2008年第2期401-404,共4页
Chinese Journal of Scientific Instrument
关键词
RBF神经网络
柴油机
热工参数
排气温度
智能检测
RBF NN
diesel engine
thermal cycling parameter
exhaust temperature
intelligent detection
