Variable Elimination-Based Contribution for Accurate Fault IdentificationThis study was supported by JSPS KAKENHI 15K06554

• Published in: IFAC-PapersOnLine Vol. 49; no. 7; pp. 383 - 388
• Main Authors: Satoyama, Yusuke, Fujiwara, Koichi, Kano, Manabu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2016
• Subjects: contribution plot; fault diagnosis; Fault identification; kernel principal component analysis; multivariate statistical process control (MSPC); nonlinear systems; process monitoring; nonlinear systems; fault diagnosis; Fault identification; contribution plot; multivariate statistical process control (MSPC); process monitoring; kernel principal component analysis
• ISSN: 2405-8963; 2405-8963
• DOI: 10.1016/j.ifacol.2016.07.368

We propose a new fault identification method, which can describe the contribution of each process variable to a detected fault and identify a faulty variable more accurately than conventional methods. In the proposed method, in addition to a fault detection model that describes normal operating condition (NOC), multiple fault identification models that describe the same NOC are also constructed by eliminating one variable from all monitored variables at a time. After a fault is detected with the fault detection model, the fault detection index, e.g. a combined index of the T2 and Q statistics, is calculated by using each of the fault identification models. When the faulty variable is eliminated, the index does not change before and after the fault occurs. On the other hand, when the normal variable is eliminated, the index is affected by the fault and increases after the fault occurs. Thus, the eliminated variable corresponding to the index that does not increase after the occurrence of the fault is identified as a faulty variable. In the proposed method, the ratio of the average index in NOC to the current index is used as a fault identification index or a contribution. To validate the proposed method, VEC was compared with the reconstruction-based contribution (RBC) through numerical examples. The results have demonstrated that VEC outperformed RBC in fault identification performance both in the linear case and in the nonlinear case.