Interval-valued data correlation modeling approach for uncertain nonlinear and non-Gaussian process monitoring

Abstract

Process monitoring is a crucial part of ensuring the safety and quality of industrial production, and fault detection is a particularly critical step. As a departure from the dimensionality reduction strategy commonly used in fault detection methods, this paper aims to create a statistical model by directly extracting complex correlations among variables with nonlinearity and non-Gaussian properties. Uncertainties in measurement data in an actual process can significantly impact the control decision based on a monitoring model, so interval-valued description strategy is introduced to effectively take the uncertainties into account. Moreover, we improved upon the traditional interval-valued data generation method using moving window technology combine the receiver-operator characteristic curve to construct intervals based on sample mean and standard deviation (SD), which makes full use of the data information. This paper proposes a mean-SD interval vine copula (MSIVC) model for complex industrial process fault detection. The high density region and density quantile theory are introduced to determine the control boundary. The process monitoring performance of the MSIVC method is evaluated by a numerical example and the Tennessee-Eastman process. The results show that the proposed model is stable, sensitive to process faults, and yields effective monitoring results.