Isolating incipient sensor fault based on recursive transformed component statistical analysis. (April 2018)

• Record Type: Journal Article
• Authors: Shang, Jun; Chen, Maoyin; Ji, Hongquan; Zhou, Donghua
• Is Part Of: Journal of process control. Volume 64(2018)
• Page Start: 112
• Page End: 122
• Publication Date: 2018-04
• Subjects: Fault isolation -- Incipient sensor fault -- Subspace reconstruction -- Subblock detection -- Recursive transformed component statistical analysis; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2018.01.002
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: This paper considers the isolation problem of incipient sensor fault. Based on recursive transformed component statistical analysis (RTCSA), two different isolation methods are proposed. The first method is called subspace reconstruction, where elements in specific subspaces are eliminated, and then reconstructed by minimizing the reconstructed detection index. The faulty variable is determined by the least scaled reconstructed detection index. The second method is called subblock detection, which has less online computational complexity. The subblocks of the measurement matrix are sequentially selected in each sliding window to calculate the subblock detection indices, and the faulty variable is determined by the largest subblock detection margin. Compared with the existing isolation methods such as reconstruction-based contribution (RBC) and its variant termed as average residual-difference reconstruction contribution plot (ARdR-CP), the superior isolation performances of the proposed methods are illustrated by a numerical example as well as a simulation on a continuous stirred tank reactor.