核动力工程
覈動力工程
핵동력공정
NUCLEAR POWER ENGINEERING
2009年
6期
86-90
,共5页
高斯混合模型(GMM)%汽轮机故障诊断%小波包分析%EM算法
高斯混閤模型(GMM)%汽輪機故障診斷%小波包分析%EM算法
고사혼합모형(GMM)%기륜궤고장진단%소파포분석%EM산법
Gaussian Mixture Models(GMM)%Turbine faults diagnosis%Wavelet packet analysis%Expectation-Maximization (EM) algorithm
采用高斯混合模型(GMM)与小波包分析相结合的方法,对汽轮机振动故障进行了诊断研究.首先对振动故障信号进行小波包分解,去除干扰信号,提取包含故障特征信息的频段作为故障特征矢量.以此特征矢量建立GMM,并用建立的模型识别各种故障.利用在Bently实验台上测得的实验数据进行建模及故障识别.计算结果中,当模数M=12时,GMM识别故障的正确率约80%～90%,表明GMM结合小波包分析进行汽轮机振动故障诊断的方法能取得较好的效果.
採用高斯混閤模型(GMM)與小波包分析相結閤的方法,對汽輪機振動故障進行瞭診斷研究.首先對振動故障信號進行小波包分解,去除榦擾信號,提取包含故障特徵信息的頻段作為故障特徵矢量.以此特徵矢量建立GMM,併用建立的模型識彆各種故障.利用在Bently實驗檯上測得的實驗數據進行建模及故障識彆.計算結果中,噹模數M=12時,GMM識彆故障的正確率約80%～90%,錶明GMM結閤小波包分析進行汽輪機振動故障診斷的方法能取得較好的效果.
채용고사혼합모형(GMM)여소파포분석상결합적방법,대기륜궤진동고장진행료진단연구.수선대진동고장신호진행소파포분해,거제간우신호,제취포함고장특정신식적빈단작위고장특정시량.이차특정시량건립GMM,병용건립적모형식별각충고장.이용재Bently실험태상측득적실험수거진행건모급고장식별.계산결과중,당모수M=12시,GMM식별고장적정학솔약80%～90%,표명GMM결합소파포분석진행기륜궤진동고장진단적방법능취득교호적효과.
The Gaussian Mixture Models and the wavelet packet analysis are used to the turbine vibration faults diagnosis. De-compound firstly the vibration faults signal and delete the disturbed component. Then, take the frequency segments which contain the fault characteristics as the fault characteristics vector. To set up the Gaussian Mixture Models with the vectors, and identify the different faults with the built model. The experiment data measured in Benlty experiment platform is adopted to set up the model and identify the faults. In the calculation results, when the modulus equal to twelve, the precision for the faults diagnosis by the Gaussian Mixture Models is approximately 80% ～ 90%. It indicates that the turbine vibration fault can be diagnosed effectively by the Gaussian Mixture Models and the wavelet packet analysis.
