Fault detection and diagnosis with parametric uncertainty using generalized polynomial chaos. (8th May 2015)

• Record Type: Journal Article
• Authors: Du, Yuncheng; Duever, Thomas A.; Budman, Hector
• Is Part Of: Computers & chemical engineering. Volume 76(2015)
• Page Start: 63
• Page End: 75
• Publication Date: 2015-05-08
• Subjects: Stochastic faults -- Fault isolation -- Diagnosability -- Uncertainty analysis -- Computational efficiency; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2015.02.009
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: A new method to identify and diagnose intermittent stochastic faults is proposed. The algorithms are successful in diagnosing both individual and simultaneous occurrence of multiple stochastic faults. The distinguishability of faults is quantitative assessed by type I and type II analysis. The method illustrates its advantages in term of computational efficiency as well as accuracy. Abstract: This paper presents a new methodology to identify and diagnose intermittent stochastic faults occurring in a process. A generalized polynomial chaos (gPC) expansion representing the stochastic inputs is employed in combination with the nonlinear mechanistic model of the process to calculate the resulting statistical distribution of measured variables that are used for fault detection and classification. A Galerkin projection based stochastic finite difference analysis is utilized to transform the stochastic mechanistic equation into a coupled deterministic system of equations which is solved numerically to obtain the gPC expansion coefficients. To detect and recognize faults, the probability density functions (PDFs) and joint confidence regions (JCRs) of the measured variables to be used for fault detection are obtained by substituting samples from a random space into the gPC expansions. The method is applied to a two dimensional heat transfer problem with faults consisting of stochastic changes combined with step change variations in the thermal diffusivity and in a boundary condition. The proposed methodology is compared with a Monte Carlo (MC) simulations based approach to illustrate its advantages in terms of computational efficiency as well as accuracy.