Nonlinear Gaussian Belief Network based fault diagnosis for industrial processes
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]
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.