摘要
提出一种利用高斯混合模型对汽轮机振动故障进行诊断的方法。原始的汽轮机振动故障信号用小波包进行分解重构滤波,提取振动信号特征量,然后用特征量来建立高斯混合模型。用每种故障状态的几组数据作训练数据,对每种故障状态建立一个识别元,识别元的参数用EM算法求解最大似然估计,最终将待识别故障数据输入每个识别元,找到最大概率的识别元所对应的故障即为诊断的最后结果。
A turbine vibration fauhs diagnosis method by using Gaussian Mixture Models was proposed. The original turbine vibration faults signal is decomposed and reconstructed by wavelet packet analysis method, which act as a filter. Then the character of the vibration signal is picked up and used to set up the GMM. For each fault situation, taking its several set of the fault data as training data, an identifying cell for this fault situation is created. The maximum likelihood estimation of parameter of identifying cell is solved with EM algorithm. At last, the unidentified data is input to every identifying cell, and the maximum probability cell is found out, and the fault of this cell is the last diagnosis result.
出处
《华电技术》
CAS
2008年第12期21-23,共3页
HUADIAN TECHNOLOGY
