计算机工程与应用Issue(18)：257-260,4.DOI:10.3778/j.issn.1002-8331.1210-0318
稀疏特征选择在过程工业故障诊断中的应用
Sparse feature selection method for fault diagnosis of process industry
摘要
Abstract
In this paper, a new sparse representation based feature selection method is proposed, in which the sample matrix is composed of the mean and variant of training sample, and testing sample is the index vector responding to sample matrix. Homotopy algorithm is used to solve the optimization problem. Traditional selecting methods based on wavelet package decomposition and Bhattacharyya distance methods, and recently used sparse methods, sparse representation classifier, sparsity preserving projection and sparse principal component analysis, are compared to the proposed method. Simulations show the proposed selecting method gives the improved performance on fault diagnosis with Tennessee Eastman Process data.关键词
稀疏表达/特征选择/故障诊断/过程工业Key words
sparse representation/feature selection/fault diagnosis/process industry分类
信息技术与安全科学引用本文复制引用
于春梅..稀疏特征选择在过程工业故障诊断中的应用[J].计算机工程与应用,2014,(18):257-260,4.基金项目
国家自然科学基金（No.60802040）。 （）
