A Distributed Fault Detection Filtering Approach for a Class of Interconnected Input-Output Nonlinear Systems

This paper develops a filtering approach for distributed fault detection of a class of interconnected input-output nonlinear systems with modeling uncertainties, disturbances and measurement noise. A distributed fault detection filtering scheme and the corresponding adaptive thresholds are designed based on filtering certain signals so that the effect of the measurement noise and disturbances is attenuated, which facilitates less conservative thresholds and enhanced robustness. Further analysis leads to a quantitative characterization of the class of detectable faults and simulation results are used to illustrate the proposed distributed fault diagnosis filtering approach

Fault diagnosis of a class of nonlinear uncertain systems with Lipschitz nonlinearities using adaptive estimation

This paper presents a fault detection and isolation (FDI) scheme for a class of Lipschitz nonlinear systems with nonlinear and unstructured modeling uncertainty. This significantly extends previous results by considering a more general class of system nonlinearities which are modeled as functions of the system input and partially measurable state variables. A new FDI method is developed using adaptive estimation techniques. The FDI architecture consists of a fault detection estimator and a bank of fault isolation estimators. The fault detectability and isolability conditions, characterizing the class of faults that are detectable and isolable by the proposed scheme, are rigorously established. The fault isolability condition is derived via the so-called fault mismatch functions, which are defined to characterize the mutual difference between pairs of possible faults. A simulation example of a single-link flexible joint robot is used to illustrate the effectiveness of the proposed sche

An Algebraic Approach for Robust Fault Detection of Input-Output Elastodynamic Distributed Parameter Systems

This paper deals with the problem of designing a robust fault detection methodology for a class of input-output, uncertain dynamical distributed parameter systems, namely mechanical elastodynamic systems, which are representative of a whole class of problems related to on-line health monitoring of mechanical and civil engineering structures. The proposed approach does not require full state measurements and is robust to measuring, modeling and numerical errors, thanks to a time varying detection threshold. In order to avoid the problems associated with classical discretization techniques for distributed parameter systems, which can lead to numerical errors difficult to bound a priori, and thus higher thresholds, a suitable structure-preserving algebraic approach, called Cell Method, will be employed. This method consists in writing the equations of a distributed parameter system directly in discrete form, avoiding the usual discretization process and leading to a symplectic, that is energy preserving, numerical scheme