无线电工程
無線電工程
무선전공정
Radio Engineering
2015年
9期
72-76
,共5页
仝奇%胡双演%李钊%叶霞%张仲敏
仝奇%鬍雙縯%李釗%葉霞%張仲敏
동기%호쌍연%리쇠%협하%장중민
核主元分析%BP神经网络%齿轮泵%特征提取%故障诊断
覈主元分析%BP神經網絡%齒輪泵%特徵提取%故障診斷
핵주원분석%BP신경망락%치륜빙%특정제취%고장진단
kernel principal component analysis%back propagation neural network%gear pump%feature extraction%fault diagnosis
针对神经网络结构复杂和训练时间长的问题,提出了一种基于核主元分析的反向传播神经网络齿轮泵故障诊断方法。使用经验模态分解对采集的齿轮泵振动信号进行特征分解形成原始特征参数集,利用核主元分析法提取信号的非线性特征,降低样本维数,并将结果作为神经网络的输入训练齿轮泵故障诊断模型,对测试样本进行诊断。实验结果表明,该方法对齿轮泵样本能够有效聚类,降低网络复杂度,减少网络训练时间和次数,并提高故障诊断的精度。
針對神經網絡結構複雜和訓練時間長的問題,提齣瞭一種基于覈主元分析的反嚮傳播神經網絡齒輪泵故障診斷方法。使用經驗模態分解對採集的齒輪泵振動信號進行特徵分解形成原始特徵參數集,利用覈主元分析法提取信號的非線性特徵,降低樣本維數,併將結果作為神經網絡的輸入訓練齒輪泵故障診斷模型,對測試樣本進行診斷。實驗結果錶明,該方法對齒輪泵樣本能夠有效聚類,降低網絡複雜度,減少網絡訓練時間和次數,併提高故障診斷的精度。
침대신경망락결구복잡화훈련시간장적문제,제출료일충기우핵주원분석적반향전파신경망락치륜빙고장진단방법。사용경험모태분해대채집적치륜빙진동신호진행특정분해형성원시특정삼수집,이용핵주원분석법제취신호적비선성특정,강저양본유수,병장결과작위신경망락적수입훈련치륜빙고장진단모형,대측시양본진행진단。실험결과표명,해방법대치륜빙양본능구유효취류,강저망락복잡도,감소망락훈련시간화차수,병제고고장진단적정도。
Aiming at the complex structures and time?consuming problem of neural network,this paper proposes a gear pump fault diagnosis method based on kernel principal component analysis (KPCA) and back propagation neural network (BPNN).Firstly,empiri?cal mode decomposition ( EMD) is used to break down the acquired gear pump vibration signal characteristic to form the original char?acteristic parameter set.Secondly,KPCA is used to extract nonlinear feature of the signal and reduce the sample dimensions.Finally,the results can be used as the input of BPNN to train the gear pump fault diagnosis model for diagnosis of the test samples.The experimental results show that the method can effectively realize clustering of gear pump samples,reduce the network complexity,cut down the net?work training time and times,and improve the accuracy of fault diagnosis.