Fault Detection Method Based on Global-Local Marginal Discriminant Preserving Projection for Chemical Process

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.

A Review on Data-Driven Process Monitoring Methods: Characterization and Mining of Industrial Data

“…More recently, Li et al proposed a global-local marginal discriminant preserving projection method, by which GLPP was integrated with multiple marginal fisher analysis to extract the discriminative feature of historical fault data. This method can better separate normal data from fault data under the circumstance that the fault data is available [120]. The manifold learning-based methods have an ability to describe nonlinear features through linear approximation projection.…”

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

“…Examples of applications in the process monitoring field include: a method based on kernel functions specially designed for batch processes [500]; use of manifold learning in conjunction with clustering methods for temporal alignment and phase identification of batch processes [501]; a new monitoring index for control charts based on manifold learning [502]. A recent trend is the proposition of methodologies that simultaneously represent local and global structures in the data [503][504][505][506][507][508][509].…”