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Fault isolation and alarm design in non-linear stochastic systems

University of British Columbia

In this project, first we propose a novel model-based algorithm for fault detection and isolation (FDI) in stochastic non-linear systems. The algorithm is established based on parameter estimation by monitoring any changes in the behaviour of the process and identifying the faulty model using a bank of particle filters running in parallel with the process model. The particle filters are used to generate a sequence of hidden states, which are then used in a log-likelihood ratio to detect and isolate the faults. The newly developed scheme is demonstrated through implementation in two highly non-linear case studies. Finally, the effectiveness and robustness of the proposed diagnostic algorithm are illustrated by comparing the results obtained by applying the algorithm to the multi-unit chemical reactor system using other FDI techniques, based on EKF and UKF state estimators. Second, we propose an approach based on particle filter algorithm to isolate actuator and sensor faults in stochastic non-linear and non-Gaussian systems. The proposed FDI approach is based on a state estimation approach using a general observer scheme (GOS), whereby a bank of particle filters is used to generate a set of residuals, each sensitive to all but one fault. The faults are then isolated by monitoring the behaviour of the residuals where the residuals of the faulty sensors or actuators behave differently than the faultless residuals. The approach is demonstrated through implementing two highly non-linear case studies. Non-linear stochastic systems pose two important challenges for designing alarms : (1) measurements are not necessarily Gaussian distributed and (2) measurements are correlated - in particular, for closed-loop systems. We therefore present an algorithm for designing alarms based on delay timers and deadband techniques for such systems, with unknown and known models. In the case of unknown models, our approach is based on Monte Carlo simulations. In the case of known models, it makes use of a probability density function approximation algorithm called particle filtering. The alarm design algorithm is illustrated through two simulation examples. We show that the proposed alarm design is effective in detecting the fault, even though the measurements are non-Gaussian.

Text

2015-02-02

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/52010

Chemical and Biological Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/