Abstract
Feature extraction plays a key role in fault detection methods. Most existing methods focus on comprehensive and accurate feature extraction of normal operation data to achieve better detection performance. However, discriminative features based on historical fault data are usually ignored. Aiming at this point, a global-local marginal discriminant preserving projection (GLMDPP) method is proposed for feature extraction. Considering its comprehensive consideration of global and local features, global-local preserving projection (GLPP) is used to extract the inherent feature of the data. Then, multiple marginal fisher analysis (MMFA) is introduced to extract the discriminative feature, which can better separate normal data from fault data. On the basis of fisher framework, GLPP and MMFA are integrated to extract inherent and discriminative features of the data simultaneously. Furthermore, fault detection methods based on GLMDPP are constructed and applied to the Tennessee Eastman (TE) process. Compared with the PCA and GLPP method, the effectiveness of the proposed method in fault detection is validated with the result of TE process.
Highlights
The large scale and high complexity of modern chemical processes lead to greater challenges to the safety and stability of the processes
With the widespread application of advanced measurement instruments and distribution control system (DCS), a large amount of data are collected and stored, which provides a good basis for the development of data-based fault detection methods [2]
Regarding the absence of discriminative information in most fault detection methods based on normal condition data, a global-local marginal discriminant preserving projection (GLMDPP)-based fault detection method is proposed
Summary
The large scale and high complexity of modern chemical processes lead to greater challenges to the safety and stability of the processes. In order to solve the issues of MSPM and manifold learning methods in features extraction, several feature extraction methods which can extract global and local features of data simultaneously were proposed and applied to fault detection. The PCA model in the above methods required data with Gaussian distribution For this issue, Luo proposed a unified framework, namely, global-local preserving projection (GLPP) to extract global and local features based on the distance relationship between neighbors and non-neighbors entirely [16]. A novel feature extraction algorithm, which is named global-local marginal discriminant preserving projection (GLMDPP), is proposed and applied for fault detection. GLPP is a manifold learning-based feature extraction method which can preserving global and local features of data simultaneously [16]. Projection matrix A for preserving both global and local features of the data can be constructed by the eigenvectors corresponding to the d smallest eigenvalues
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