Stochastic Fault Diagnosis using a Generalized Polynomial Chaos Model and Maximum Likelihood. Issue 8 (2015)

• Record Type: Journal Article
• Authors: Du, Yuncheng; Duever, Thomas A.; Budman, Hector
• Is Part Of: IFAC-PapersOnLine. Volume 48:Issue 8(2015)
• Page Start: 1270
• Page End: 1275
• Publication Date: 2015
• Subjects: Fault identification -- Uncertainty analysis -- Nonlinear model -- Particle filter -- Estimation theory; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2015.09.143
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: A novel approach has been developed to diagnose intermittent stochastic faults by combining a generalized polynomial chaos (gPC) method with maximum likelihood estimation. The gPC is used to propagate stochastic changes in an input variable to a measured output variable from which the fault is to be inferred. The fault detection and diagnosis (FDD) problem is formulated as an inverse problem of identifying the unknown input from a maximum likelihood based fitting of the predicted and measured output variables. Simulation studies compare the proposed method with a Particle Filter (PF) to estimate the value of an unknown feed mass fraction of a chemical process. The proposed method is shown to be significantly more computational efficient and less sensitive to user defined tuning parameters than PF.