关键词: 最小绝对收缩和选择算子（lasso）, 稀疏主元分析, 状态监控, 田纳西伊斯特曼（TE）过程

Abstract: Principal Component Analysis（PCA） is a multivariate statistical technique, with a range of applications in data processing and dimensionality reduction. Over the past two decades, PCA method has also been widely applied to various kinds of industrial processes for process monitoring and fault diagnosis with some successes. Due to the increasing volumes of data, process monitoring methods which are based on PCA approaches suffer many limitations, such as great calculation loads and poor real-time performance. In this paper, a new method called Sparse Principal Component Analysis（SPCA） is developed in process monitoring, using the lasso （least absolute shrinkage and selection operator） to produce modified principal components with sparse loadings. And the SPCA can be formulated as a regression-type optimization function to achieve the main elements of choice. Furthermore, the fault detection is then performed by a detection index using model parameters, and the sparse principal component analysis is used in the Tennessee Eastman process（TE processes） monitoring for simulations. Compared with the traditional principal component analysis method, this SPCA approach builds a model based on the sparse modeling data. Therefore it can reduce the amount of calculations and improve the real time performance. As the SPCA model is applied to simulate with real data, the results show that it has better effectiveness in TE processes.

Key words: least absolute shrinkage and selection operator（lasso）, Sparse Principal Component Analysis（SPCA）, state monitoring, Tennessee Eastman（TE） processes