A novel AdaBoost ensemble model based on the reconstruction of local tangent space alignment and its application to multiple faults recognition. (August 2021)

• Record Type: Journal Article
• Authors: Xu, Yuan; Cong, Kaiduo; Zhu, Qunxiong; He, Yanlin
• Is Part Of: Journal of process control. Volume 104(2021)
• Page Start: 158
• Page End: 167
• Publication Date: 2021-08
• Subjects: Fault diagnosis -- Local tangent space -- AdaBoost -- Multiple faults -- Industrial processes; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2021.07.004
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: In order to recognize the coupling faults of complex industrial processes effectively, this paper proposed an AdaBoost ensemble (AdBE) model based on the reconstruction of local tangent space alignment (RLTSA). First, to obtain the low-dimensional manifold structure embedded in original data space, RLTSA algorithm is designed by constructing the tangent space in the neighborhood of each data point to represent the local geometry and then aligning them to obtain the embedding coordinates. Secondly, to solve the loss of global feature information, an affine matrix is used to inversely map the low-dimensional coordinates to restore the global structure information. Thirdly, based on the above reconstruction of local tangent space alignment, an AdaBoost ensemble (AdBE) classifier is constructed for multiple faults recognition in which the AdaBoost algorithm is used to improve the performance of Decision Tree (DT), and One vs. Rest (OvR) ensemble strategy is introduced to establish the RLTSA-AdBE model. Case studies are conducted using a three-dimensional S_curve data set and the Tennessee Eastman process (TEP) to respectively verify the performance of the RLTSA algorithm and the proposed RLTSA-AdBE model. The simulation results indicate that the proposed method guarantees high diagnosis accuracy and macro_F1 Score of coupling faults recognition. Highlights: AdaBoost Ensemble Based on Local Tangent Space Alignment is proposed. An affine matrix is used to map low-dimensional coordinates to restore global structures. AdaBoost ensemble (AdBE) classifier is constructed for multiple faults recognition. Case studies of multi-faults using the Tennessee Eastman process are executed. The effectiveness and feasibility of the proposed fault diagnosis method is conformed.