An improved fault detection algorithm based on wavelet analysis and kernel principal component analysis

摘要：

Original signal is decomposed by wavelet in different scales, the wavelet decomposition coefficients of the real signal are held, and the wavelet decomposition coefficients of the noise are eliminated, then the signal is reconstructed by inverse wavelet transform. Kernel PCA can eliminate the relativity of variables and extract the fault information better, the feature information of the pretreatment datum is obtained by KPCA, and the performance of fault detection is improved.

关键词： wavelet analysis kernel principal component analysis tennessee-eastman process fault detection

作者: Liang Chen Yang Yu Jie Luo Yawei Zhao

作者单位: Faculty of Information Science & Engineering, Shenyang Ligong University, Shenyang, 110159

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 1723-1726

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）