An explicit nonlinear mapping‐based locality constrained index for nonlinear statistical process monitoring. (14th September 2022)

• Record Type: Journal Article
• Authors: Chen, Yang; Tong, Chudong; Shi, Xuhua
• Is Part Of: Canadian journal of chemical engineering. Volume 101:Number 4(2023)
• Page Start: 1931
• Page End: 1942
• Publication Date: 2022-09-14
• Subjects: explicit nonlinear mapping -- feature extraction -- monitoring index -- statistical process monitoring; Chemical engineering -- Periodicals; Technology -- Periodicals; 660.05
• DOI: 10.1002/cjce.24565
• Languages: English
• ISSNs: 0008-4034

Abstract

Abstract: Different from the mainstream nonlinear multivariate statistical process monitoring approaches, which usually implement offline feature extraction for a given dataset sampled from the normal operating condition, the proposed method analyzes the inconsistency inherited in each specific online monitored sample of current interest in a timely manner so that a novel monitoring statistic called locality constrained index (LCI) can be simultaneously calculated. Through taking advantage of an explicit nonlinear mapping (ENM), the sampled data is first transformed into a higher‐dimensional space so as to explicitly reflect the inherited nonlinearity between measured variables. With the involvement of the online monitored sample in designing the corresponding objective function, the calculation of LCI is then targeted to point out the deviation within the neighbourhood. According to the comparisons with the counterparts, the proposed ENM‐LCI‐based method demonstrated its salient superiority and consistent effectiveness in nonlinear process monitoring.