摘要
为了有效地对发动机运行状态进行监测,提出了一种基于小波包和神经网络相结合的发动机故障诊断方法。以某微型车用汽油发动机为研究对象,建立基于振动信号分析的测试试验系统,采集发动机正常工况和故障工况的振动特征参数。通过小波包对其进行分解和重构,提取出表征发动机工作状况的特征向量,作为训练样本数据和检验样本数据,输入BP神经网络并对其进行训练,实现了对所设发动机故障类型进行良好识别的预期效果。
In order to effectively monitor the engine running state,a fault diagnosis method is put forward on the combination of the wavelet packet and neural network engine.Aimed at the petrol engine of a miniature car,the test pilot system based on vibration signal analysis is built,vibration characteristic parameters of the engine are collected in normal operating conditions and fault conditions.When the wavelet packet is decomposed and reconstructed,the eigenvectors are extracted to demonstrate the working conditions of the engine,as the neural network training sample data and test sample data.BP neural network is inputted and then trained,to implement the fault type identification of the engine with the desired effect.
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2013年第5期763-769,909,共7页
Journal of Vibration,Measurement & Diagnosis
基金
内蒙古自然科学基金资助项目(2012MS0704)
内蒙古高校科研基金重点资助项目(NJZZ11070)
