小波除噪、神经网络与发动机故障诊断  

Wavelet Noise Filtering, Neural Network and Engine Fault Diagnosis

作　　者：马瑞恒[1] 王新晴[1] 王耀华[1] 闫嘉昕[1] 

机构地区：[1]解放军理工大学,江苏210007

出　　处：《内燃机工程》2002年第6期38-41,46,共5页Chinese Internal Combustion Engine Engineering

摘　　要：给出了利用起动电压波形来检测气密性这一简单易行的实验原理与方法。并对代表性波形进行小波除噪处理,在此基础上,提出了6个故障信息特征参数,通过对径向基神经网络的训练和检测,证明该神经网络能够成功的进行故障模式的辨识。从而为发动机压缩性故障诊断提供一个较好的方法。In this paper an experiment method, of which main purpose was to analyze the cylinder gastightness from starting voltage waveforms was discussed. The noise was filtered from typical waveforms, and 6 fault symptom parameters were put forward. The Radial Basis Function Netword (RBFN) was trained by putting these symptom parameters in. This network can distinguish the fault modes preferably; therefore it will be favourable to diagnose the cylinder compression ratio fault.

关 键 词：小波除噪 神经网络 发动机 故障诊断 内燃机 气缸压缩机 

分 类 号：TK407[动力工程及工程热物理—动力机械及工程]