摘要
利用LabVIEW构建了基于EMD与神经网络的内燃机气门间隙故障诊断系统。用490BPG型发动机在转速为1200r/min、无负荷时进行了试验研究,采用经验模式分解EMD方法对气门振动信号进行分解,对分解得到的前4个固有模态函数IMF分别求其关联维数,将IMF1-IMF4的关联维数作为神经网络的输入向量,用4种工况的80组样本训练了内燃机气门故障诊断系统的网络模型。试验结果表明,20组测试样本的测试结果均与实际状况相一致,诊断准确率为100%,该系统能快速准确地识别内燃机气门间隙故障。
A fault diagnosis system was constructed and designed based on EMD correlation dimension, artificial neural network and LabVIEW. The experiment was done with 490BPG model engine when it worked at 1 200 r/min without load. The original vibration signals of valve were decomposed based on EMD method, and then the correlation dimensions of the top four intrinsic mode functions （IMF1 -IMF4） were calculated, the correlation dimensions of the IMF1 -IMF4 as the input parameters of the artificial neural network were taken and the network model was trained with 80 training samples of four work status, it indicated that the test results of 20 test samples of the experiment conformed to the practical status and the correct rate was 100%. The system could be used online to inspect and diagnose the faults of engine valve clearance.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2007年第12期133-136,147,共5页
Transactions of the Chinese Society for Agricultural Machinery
基金
华中农业大学科研专项(项目编号:52204-02008)
