Enhancing Data-based Fault Isolation Through Nonlinear Control

This work focuses on a broad class of nonlinear process systems subject to control actuator faults and disturbances and proposes a method for data-based fault detection and isolation that explicitly takes into account the design of the feedback control law. This method allows isolating specific faults in the closed-loop system; fault detection is done using a purely data-based approach and fault isolation is achieved using the structure of the closed-loop system as induced by an appropriately designed controller. This is achieved through the design of nonlinear model-based state-feedback control laws that decouple the dependency between certain process state variables in the closed-loop system. In this sense, the proposed approach constitutes a departure from the available data-based fault detection and isolation techniques which do not take advantage of the design of the feedback control law to enforce a closed-loop system structure that enhances fault isolation. The theoretical results are demonstrated through simulations of a CSTR and a gas-phase polyethylene reactor. 2007 American Institute of Chemical Engineers AIChE J, 54: 223–241, 2008