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Self-Organizing map based fault diagnosis technique for non-Gaussian processes

journal contribution
posted on 2023-05-18, 02:01 authored by Yu, H, Faisal KhanFaisal Khan, Vikrambhai Garaniya, Ahmad, A
A self-organizing map (SOM) based methodology is proposed for fault detection and diagnosis of processes with nonlinear and non-Gaussian features. The SOM is trained to represent the characteristics of a normal operation as a cluster in a two-dimensional space. The dynamic behavior of the process system is then mapped as a two-dimensional trajectory on the trained SOM. A dissimilarity index based on the deviation of the trajectory from the center of the cluster is derived to classify the operating condition of the process system. Furthermore, the coordinate of each best matching neuron on the trajectory is used to compute the dynamic loading of each process variable. For fault diagnosis, the contribution plot of the process variables is generated by quantifying the divergences of the dynamic loadings. The proposed technique is first tested using a simple non- Gaussian model and is then applied to monitor the simulated Tennessee Eastman chemical process. The results from both cases have demonstrated the superiority of proposed technique to the conventional principal component analysis (PCA) technique.

History

Publication title

Industrial & Engineering Chemistry Research

Volume

53

Issue

21

Pagination

8831-8843

ISSN

1520-5045

Department/School

Australian Maritime College

Publisher

American Chemical Society

Place of publication

United States

Rights statement

Copyright 2014 American Chemical Society

Repository Status

  • Restricted

Socio-economic Objectives

Expanding knowledge in the mathematical sciences

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