关键词:

SVDD, 特征样本, LTSA, 过程监控

Abstract:

Dozens of advantages have been reported on using support vector data description (SVDD) in the fields of non-Gaussian process monitoring and fault diagnosis.However, during the SVDD model construction offline, usually the whole training data set is used.Due to the tremendous size of the data set, the computation burden for modeling is quite heavy, which leads to the difficulty in updating model online.Therefore, this paper proposes a fast SVDD algorithm on the basis of the feature samples.In this new algorithm, the feature samples are used in stead of the whole training data set for modeling in order to significantly reduce the computation complexity.Concurrently, PCA is replaced by the local tangent space alignment (LTSA) to extract the underlying manifold structure of the process data set, since the traditional dimension reduction methods, such as PCA, have poor capability to handle nonlinearity.Next SVDD is applied on the manifold.At last, corresponding statistical indices are used for fault detection purpose.The proposed method has been tested on the Tennessee Eastman (TE) process, while the simulation results show the efficiency of it.

Key words:

SVDD, 特征样本, LTSA, 过程监控