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Nonlinear Gaussian Belief Network based fault diagnosis for industrial processes
Citation
Yu, H and Khan, F and Garaniya, V, Nonlinear Gaussian Belief Network based fault diagnosis for industrial processes, Journal of Process Control, 35 pp. 178-200. ISSN 0959-1524 (2015) [Refereed Article]
Copyright Statement
Copyright 2015 Elsevier Ltd.
DOI: doi:10.1016/j.jprocont.2015.09.004
Abstract
A Nonlinear Gaussian Belief Network (NLGBN) based fault diagnosis technique is proposed for industrial
processes. In this study, a three-layer NLGBN is constructed and trained to extract useful features from
noisy process data. The nonlinear relationships between the process variables and the latent variables
are modelled by a set of sigmoidal functions. To take into account the noisy nature of the data, model
variances are also introducedto boththeprocess variables andthe latent variables. The three-layer NLGBN
is first trained with normal process data using a variational Expectation and Maximization algorithm.
During real-time monitoring, the online process data samples are used to update the posterior mean of
the top-layer latent variable. The absolute gradient denoted as G-index to update the posterior mean is
monitored for fault detection. A multivariate contribution plot is also generated based on the G-index for
fault diagnosis. The NLGBN-based technique is verified using two case studies. The results demonstrate
that the proposed technique outperforms the conventional nonlinear techniques such as KPCA, KICA,
SPA, and Moving Window KPCA.
Item Details
Item Type: | Refereed Article |
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Keywords: | Online fault diagnosis, Nonlinear and noisy processes, Nonlinear Gaussian Belief Network, PCA, KPCA, KICA, SPA, MWKPCA |
Research Division: | Engineering |
Research Group: | Chemical engineering |
Research Field: | Process control and simulation |
Objective Division: | Energy |
Objective Group: | Environmentally sustainable energy activities |
Objective Field: | Environmentally sustainable energy activities not elsewhere classified |
UTAS Author: | Yu, H (Mr Hongyang Yu) |
UTAS Author: | Khan, F (Professor Faisal Khan) |
UTAS Author: | Garaniya, V (Associate Professor Vikram Garaniya) |
ID Code: | 103663 |
Year Published: | 2015 |
Web of Science® Times Cited: | 39 |
Deposited By: | NC Maritime Engineering and Hydrodynamics |
Deposited On: | 2015-10-23 |
Last Modified: | 2017-11-03 |
Downloads: | 0 |
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