ROBUST NONLINEAR FAULT-DIAGNOSIS IN INPUT-OUTPUT SYSTEMS

The design and analysis of fault diagnosis architectures using the mod el-based analytical redundancy approach has received considerable atte ntion during the last two decades. One of the key issues in the design of such fault diagnosis schemes is the effect of modelling uncertaint ies on their performance. This paper describes a fault diagnosis algor ithm for a class of nonlinear dynamic systems with modelling uncertain ties when not all states of the system are measurable. The main idea b ehind this approach is to monitor the plant for any off-nominal system behaviour due to faults utilizing a nonlinear online approximator wit h adjustable parameters. The online approximator only uses the system input and output measurements. A nonlinear estimation model and learni ng algorithm are described so that the online approximator provides an estimate of the fault. The robustness, sensitivity, stability and per formance properties of the nonlinear fault diagnosis scheme are rigoro usly established under certain assumptions on the failure type. A simu lation example of a simple second-order system is used to illustrate t he robust nonlinear fault diagnosis scheme.