Novel fault subspace extraction methods for the reconstruction-based fault diagnosis. (September 2021)

• Record Type: Journal Article
• Authors: Hu, Changhua; Luo, Jiayu; Kong, Xiangyu; Feng, Xiaowei
• Is Part Of: Journal of process control. Volume 105(2021)
• Page Start: 129
• Page End: 140
• Publication Date: 2021-09
• Subjects: Improved PLS (IPLS) -- Fault diagnosis -- Fault reconstruction -- General principal component analysis (GPCA); Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2021.07.008
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: In fault diagnosis, partial least squares (PLS) is a popular data-driven method to identify abnormal key performance indicators (KPI). However, there are two problems in fault diagnosis when using PLS, including inaccurate fault subspace extraction and unidentified false alarms. In the first problem, the improved PLS (IPLS) model is adopted to obtain a precise subspace through orthogonal decomposition. In addition, to eliminate the normal value in fault data, a quality-related fault subspace (QRFS) extraction method is proposed, which can extract a purer quality-related fault subspace. In the second problem, to provide feedback for false alarms, a modified IPLS (M-IPLS) model is proposed to extract the quality-unrelated fault subspace. Based on the proposed fault subspace extraction methods, the fault can be reconstructed by a lower dimensional fault subspace and false alarms with feedback can improve the efficiency of diagnosis. Finally, two examples, including a numerical simulation and the Tennessee Eastman process (TEP), are used to show the effectiveness of the proposed method. Highlights: More fault characters are extracted by the novel quality-related fault subspace. Modified model is proposed to separate redundant information. The normal information in fault process is eliminated using generalized PCA. The proposed strategy provides the feedback for false alarms. Quality-related fault type can be identified from known datasets.