振动与冲击
振動與遲擊
진동여충격
JOURNAL OF VIBRATION AND SHOCK
2015年
1期
145-150
,共6页
协整%故障诊断%非平稳%支持向量机(SVM)%液压舵面系统
協整%故障診斷%非平穩%支持嚮量機(SVM)%液壓舵麵繫統
협정%고장진단%비평은%지지향량궤(SVM)%액압타면계통
cointegration%non-stationary%supporting vector machine (SVM)%hydraulic flap servo system
对于非平稳工程系统,若系统变量是一阶单整的,则可以建立变量间的协整关系模型,通过理论分析证明,协整系数矩阵可作为故障空间的特征参数用作系统的故障诊断。非平稳系统变量首先要通过单位根检验以证明其一阶单整,然后使用 Johansen 检验法估计出协整系数矩阵,即特征参数,使用一对多分类的支持向量机(SVM)算法进行故障数据的训练和测试。使用液压舵面故障仿真系统作为试验平台,使用输入指令、舵面角度等5个一阶单整的系统变量作为协整变量,其估计出的协整系数矩阵作为特征参数,结果表明协整系数矩阵作为特征参数,SVM作为数据分类方法,具有很好的故障分类效果。
對于非平穩工程繫統,若繫統變量是一階單整的,則可以建立變量間的協整關繫模型,通過理論分析證明,協整繫數矩陣可作為故障空間的特徵參數用作繫統的故障診斷。非平穩繫統變量首先要通過單位根檢驗以證明其一階單整,然後使用 Johansen 檢驗法估計齣協整繫數矩陣,即特徵參數,使用一對多分類的支持嚮量機(SVM)算法進行故障數據的訓練和測試。使用液壓舵麵故障倣真繫統作為試驗平檯,使用輸入指令、舵麵角度等5箇一階單整的繫統變量作為協整變量,其估計齣的協整繫數矩陣作為特徵參數,結果錶明協整繫數矩陣作為特徵參數,SVM作為數據分類方法,具有很好的故障分類效果。
대우비평은공정계통,약계통변량시일계단정적,칙가이건립변량간적협정관계모형,통과이론분석증명,협정계수구진가작위고장공간적특정삼수용작계통적고장진단。비평은계통변량수선요통과단위근검험이증명기일계단정,연후사용 Johansen 검험법고계출협정계수구진,즉특정삼수,사용일대다분류적지지향량궤(SVM)산법진행고장수거적훈련화측시。사용액압타면고장방진계통작위시험평태,사용수입지령、타면각도등5개일계단정적계통변량작위협정변량,기고계출적협정계수구진작위특정삼수,결과표명협정계수구진작위특정삼수,SVM작위수거분류방법,구유흔호적고장분류효과。
In terms of the fault diagnosis for non-stationary engineering systems,the cointegration coefficients matrix was proposed as the characteristic parameter if the system variables are integrated of first order.System variables should first be verified to be integrated of first order through unit root test,then Johansen method was used to estimate the cointegration coefficients matrix.The classification algorithm of supporting vector machine (SVM)was used to train the model and test the accuracy of classification.Thus,a new approach applied in non-stationary system was established towards finding a new characteristic parameter for fault diagnosis.This new approach has been applied to the fault diagnosis of a simulated hydraulic flap servo system,using 5 system variables including input orders,flap angle,etc.as the cointegration variables.The test results indicate that the cointegration coefficients matrix model has great fault diagnosis ability for typical non-stationary systems.