Robust model-based fault detection and isolation for nonlinear processes using sliding modes

This paper proposes a robust fault detection and isolation system for nonlinear processes that can be formulated as differential algebraic equations. For open-loop stable or closed-loop stabilized systems that operate under strict nonlinear detectability conditions, a methodology to design a nonlinear state estimator based on sliding mode theory was proposed. The extended observer can handle both parameter estimation and parameters with uncertainties. As a result, the state estimator is able to follow the faulty system, detecting faults by examining changes in the controlled outputs with respect to setpoint and then probing variations in the parameters estimated. Once the fault has occurred, the isolation mechanism uses the information provided from the state estimator, generated from recovery actions in the presence of a fault. These differences from normal operation trends can be derived through statistical analysis and then can be used to identify faults. A steam generator system was used to validate this approach, where process faults were considered. The proposed robust fault detection and isolation method shows significant advantages when applied to nonlinear model systems with parameter uncertainties or with complex nonlinearities. The complex nonlinearities can be simplified with algebraic nonlinear functions that have bounded uncertainties in their parameters. Copyright (C) 2011 John Wiley & Sons, Ltd.