Risk-based fault detection and diagnosis for nonlinear and non-Gaussian process systems using R-vine copula

Abstract

This paper presents a risk-based fault detection and diagnosis methodology for nonlinear and non-Gaussian process systems using the R-vine copula and the event tree. The R-vine model provides a multivariate probability that is used in the event tree to generate a dynamic risk profile. An abnormal situation is detected from the monitored risk profile; subsequently, root cause(s) diagnosis is carried out. A fault diagnosis module is also proposed using the density quantiles, developed from marginal probabilities. The performance of this methodology is benchmarked using the Tennessee Eastman chemical process. The proposed risk-based framework has also been applied to an experimental setup and a real industrial isomer separator unit. The diagnosis module is found sensitive to both single and simultaneous faults. The results confirm that the proposed methodology provides better performance than the conventional principal component analysis and transfer entropy-based fault diagnosis techniques using the advantage of marginal density quantile analysis.