A data-driven multiplicative fault diagnosis approach for automation processes

Haiyang Hao; Kai Zhang; Steven X Ding; Zhiwen Chen; Yaguo Lei

doi:10.1016/j.isatra.2013.12.022

A data-driven multiplicative fault diagnosis approach for automation processes

ISA Trans. 2014 Sep;53(5):1436-45. doi: 10.1016/j.isatra.2013.12.022. Epub 2014 Jan 14.

Authors

Haiyang Hao¹, Kai Zhang², Steven X Ding², Zhiwen Chen², Yaguo Lei³

Affiliations

¹ 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.
² Institute for Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Bismarckstrasse 81 BB, 47057 Duisburg, Germany.
³ State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

PMID: 24434125
DOI: 10.1016/j.isatra.2013.12.022

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.

Keywords: Data-driven methods; Key performance indicator; Large-scale systems; Multiplicative fault diagnosis; Multivariate statistics; Process monitoring.