中国机械工程
中國機械工程
중국궤계공정
CHINA MECHANICAl ENGINEERING
2015年
1期
74-78
,共5页
局部切空间排列%K-最近邻分类器%模式识别%故障诊断
跼部切空間排列%K-最近鄰分類器%模式識彆%故障診斷
국부절공간배렬%K-최근린분류기%모식식별%고장진단
local tangent space alignment(LTSA)%K-nearest neighbor(KNN)%pattern recogni-tion%fault diagnosis
为了解决大型机械设备故障数据难以准确快速提取的问题，提出了一种基于局部切空间排列(LTSA)和K-最近邻分类器的转子故障诊断模型。首先基于转子的振动信号构造一个高维多征兆矩阵，利用LTSA提取高维矩阵的低维特征向量，映射在可视空间里；然后将提取的低维特征向量输入K-最近邻分类器进行故障模式识别。试验和数据降维仿真过程表明，该模型的准确度和快速性均优于LTSA和神经网络以及LTSA和支持向量机组成的故障诊断模型。
為瞭解決大型機械設備故障數據難以準確快速提取的問題，提齣瞭一種基于跼部切空間排列(LTSA)和K-最近鄰分類器的轉子故障診斷模型。首先基于轉子的振動信號構造一箇高維多徵兆矩陣，利用LTSA提取高維矩陣的低維特徵嚮量，映射在可視空間裏；然後將提取的低維特徵嚮量輸入K-最近鄰分類器進行故障模式識彆。試驗和數據降維倣真過程錶明，該模型的準確度和快速性均優于LTSA和神經網絡以及LTSA和支持嚮量機組成的故障診斷模型。
위료해결대형궤계설비고장수거난이준학쾌속제취적문제，제출료일충기우국부절공간배렬(LTSA)화K-최근린분류기적전자고장진단모형。수선기우전자적진동신호구조일개고유다정조구진，이용LTSA제취고유구진적저유특정향량，영사재가시공간리；연후장제취적저유특정향량수입K-최근린분류기진행고장모식식별。시험화수거강유방진과정표명，해모형적준학도화쾌속성균우우LTSA화신경망락이급LTSA화지지향량궤조성적고장진단모형。
In order to solve the problem that the large mechanical equipment failure data is diffi-cult to accurately extract,this paper put forward a kind of rotor fault diagnosis models based on LTSA and KNN.The vibration signals of rotor structure were used to construct dimensional matrix, then the low dimensional feature vector of high dimension matrix in the LTSA was extracted,and pro-j ected into the visual space.And the extracted low dimensional feature vectors were put into the KNN in order to do fault pattern recognition.Finally,experimental and data dimension reduction simula-tion process shows that the accuracy and rapidity of the method with LTSA and KNN are better than the fault diagnosis model by neural network and support vector machine.
