Resumen de Integration Techniques of Fault Detection and Isolation Using Interval Observers
Jordi Meseguer Amela
An interval observer has been illustrated to be a suitable approach to detect and isolate faults affecting complex dynamical industrial systems. Concerning fault detection, interval observation is an appropriate passive robust strategy to generate an adaptive threshold to be used in residual evaluation when model uncertainty is located in parameters (interval model). In such approach, the observer gain is a key parameter since it determines the time evolution of the residual sensitivity to a fault and the minimum detectable fault. This thesis illustrates that the whole fault detection process is ruled by the dynamics of the fault residual sensitivity functions and by the time evolution of the adaptive threshold related to the interval observer. Besides, it must be taken into account that these two observer fault detection properties depend on the used observer gain. As a consequence, the observer gain becomes a tuning parameter which allows enhancing the observer fault detection performance while avoiding some drawbacks related to the analytical models, as the wrapping effect. In this thesis, the effect of the observer gain on fault detection and how this parameter can avoid some observer drawbacks (i.e. wrapping effect) are deeply analyzed. One of the results of this analysis is the determination of the minimum detectable fault function related to a given fault type. This function allows introducing a fault classification according to the fault detectability time evolution: permanently (strongly) detected, non-permanently (weakly) detected or just non-detected. In this fault detection part of this thesis, two examples have been used to illustrate the derived results: a mineral grinding-classification process and an industrial servo actuator. Concerning the interface between fault detection and fault isolation, this thesis shows that both modules can not be considered separately since the fault detection process has an important influence on the fault isolation result. This influence is not only due to the time evolution of the fault signals generated by the fault detection module but also to the fact that the fault residual sensitivity functions determines the faults which are affecting a given fault signal and the dynamics of this fault signal for each fault. This thesis illustrates this point suggesting that the interface between fault detection and fault isolation must consider a set of fault signals properties: binary property, sign property, fault residual sensitivity property, occurrence order property and occurrence time instant property. Moreover, as a result of the influence of the observer gain on the fault detection stage and on the fault residual sensitivity functions, this thesis demonstrates that the observer gain has also a key role in the fault isolation module which might allow enhancing its performance when this parameter is tuned properly (i.e. fault distinguishability may be increased). As a last point, this thesis analyzes the timed discrete-event nature of the fault signals generated by the fault detection module. As a consequence, it suggests using timed discrete-event models to model the fault isolation module. This thesis illustrates that this kind of models allow enhancing the fault isolation result. Moreover, as the monitored system is modelled using an interval observer, this thesis shows as this qualitative fault isolation model can be built up on the grounds of this system analytical model. Finally, the proposed fault isolation method is applied to detect and isolate faults of the Barcelona’s urban sewer system limnimeters. Keywords: Fault Detection, Fault Diagnosis, Robustness, Observers, Intervals, Discrete-event Systems.
