Nonlinear Process Fault Detection Method using Slow Feature Analysis

DOI

10.2991/isci-15.2015.112How to use a DOI?

Keywords

Slow Feature Analysis; Fault Detection; Process Monitoring; TE Process; PCA

Abstract

Slow feature analysis (SFA) is a method that extracts the invariant or slowly varying features from an input signal based on a nonlinear expansion of it. This paper introduces SFA into industrial process monitoring. It overcomes the innate drawback of principal component analysis (PCA) that it fails to draw the more complex features or underlying nonlinear structure of the industrial process signals. Moreover, the invariance and slowness indicate the intrinsic properties of data. Thus the extracted information is interesting for data analysis. For the purpose of fault detection, two statistics are constructed: the T2 statistic and the SPE statistic. Then, these two statistics are applied to perform process monitoring. Simulations are run on the Tennessee Eastman (TE) process and the results illustrate the effectiveness of the proposed method.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yirong Lu
AU  - Dan Wang
AU  - Yupu Yang
PY  - 2015/01
DA  - 2015/01
TI  - Nonlinear Process Fault Detection Method using Slow Feature Analysis
BT  - Proceedings of the 2015 International Symposium on Computers & Informatics
PB  - Atlantis Press
SP  - 847
EP  - 853
SN  - 2352-538X
UR  - https://doi.org/10.2991/isci-15.2015.112
DO  - 10.2991/isci-15.2015.112
ID  - Lu2015/01
ER  -