摘要
针对传统的依靠单一参数对发动机进行状态监测的不足,利用模糊神经网络处理不确定性复杂问题的能力,融合多种监测参数的信息,建立了基于趋势分析的5级状态监测警报系统。首先从铁谱、振动和性能参数三方面优选监测参数,通过对大量历史数据的统计分析得到各监测参数的界限值,建立了发动机失效程度逐级递进的5级状态监测标准。然后建立隶属函数,通过计算隶属度实现了输入样本的模糊化。最后设计神经网络的结构,利用历史数据训练网络。通过对实例结果的分析证明了该模型的实效性。
Due to the defect of traditional engine condition monitoring depending on single parameter, the five-level condition monitoring alert system with fuzzy neural network （FNN） was established which is good at settling uncertainty and complicated problems. Optimal monitoring parameters were selected from the aspects of ferrography, vibration and performance parameters. With sufficient historical data, limit values of parameters and reliable five-level condition monitoring standards were maintained and established by statistical analysis. Fuzzy membership functions were applied to transfer practical data into fuzzy data. The structure of neural network was designed and trained by sample data. The model was tested with original data and proved to be effective and reliable.
出处
《润滑与密封》
CAS
CSCD
北大核心
2009年第7期74-76,共3页
Lubrication Engineering
关键词
发动机状态监测
监测标准
模糊神经网络
隶属函数
engine condition monitoring
monitoring standards
fuzzy neural network （FNN）
fuzzy membership functions
