摘要
小波包具有良好的去噪效果和高频分析能力,而概率神经网络具有很好的分类效果。采用小波包分解重构液压泵故障特征信号,并提取第三层各频率段的节点能量作为特征向量,将特征向量概率神经网络模型的输入向量对液压泵故障模式进行识别。通过采用LabVIEW和MATLAB混合编写的识别软件系统对液压泵故障识别,证明了将该方法用在液压泵故障模式识别上,能取得良好的效果。
Wavelet packet is good at de-noising and analyzing high frequency signal. Moreover the probability neural network can be well used to classify. The wavelet packets were decomposed and used to reconstruct the failure signal of hydraulic pump characteris-tics,and the node energy in each frequency bang at third layer was extracted and used to group as feature vectors. A probability neural network of the feature vectors was modeled and input as vectors to recognize the failure model of hydraulic pump. Labview and MAT-LAB were used in integration to program a recognition software system to do failure recognition of hydraulic pump. Experimental results show that the method is good at model recognition of hydraulic pump,and has achieved good effects.
出处
《机床与液压》
北大核心
2014年第13期168-170,共3页
Machine Tool & Hydraulics
基金
国家自然科学基金资助项目(51275093)
关键词
液压泵
模式识别
小波包
概率神经网络
Hydraulic pump
Model recognition
Wavelet packet
Probability neural network
