title: A data-driven multiplicative fault diagnosis approach for automation processes.
abstract: This paper presents a new data-driven method for diagnosing multiplicative key performance degradation in automation processes. Different from the well-established additive fault diagnosis approaches, the proposed method aims at identifying those low-level components which increase the variability of process variables and cause performance degradation. Based on process data, features of multiplicative fault are extracted. To identify the root cause, the impact of fault on each process variable is evaluated in the sense of contribution to performance degradation. Then, a numerical example is used to illustrate the functionalities of the method and Monte-Carlo simulation is performed to demonstrate the effectiveness from the statistical viewpoint. Finally, to show the practical applicability, a case study on the Tennessee Eastman process is presented.
authors.affiliation: Institute for Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Bismarckstrasse 81 BB, 47057 Duisburg, Germany. Electronic address: haiyang.hao@uni-due.de.
authors.forename: Haiyang
authors.initials: H
authors.lastname: Hao
authors.affiliation: Institute for Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Bismarckstrasse 81 BB, 47057 Duisburg, Germany.
authors.forename: Kai
authors.initials: K
authors.lastname: Zhang
authors.affiliation: Institute for Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Bismarckstrasse 81 BB, 47057 Duisburg, Germany.
authors.forename: Steven X
authors.initials: SX
authors.lastname: Ding
authors.affiliation: Institute for Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Bismarckstrasse 81 BB, 47057 Duisburg, Germany.
authors.forename: Zhiwen
authors.initials: Z
authors.lastname: Chen
authors.affiliation: State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
authors.forename: Yaguo
authors.initials: Y
authors.lastname: Lei
keywords: Data-driven methods; Key performance indicator; Large-scale systems; Multiplicative fault diagnosis; Multivariate statistics; Process monitoring