ABSTRACT:In this work, a model based fault diagnosis methodology for PEM fuel cell systems is presented. The methodology is based on computing residuals, indicators that are obtained comparing measured inputs and outputs with analytical relationships, which are obtained by system modelling. The innovation of this methodology is based on the characterization of the relative residual fault sensitivity. To illustrate the results, a non-linear fuel cell simulator proposed in the literature is used, with modifications, to include a set of fault scenarios proposed in this work. Finally, it is presented the diagnosis results corresponding to these fault scenarios. It is remarkable that with this methodology it is possible to diagnose and isolate all the faults in the proposed set in contrast with other well known methodologies which use the binary signature matrix of analytical residuals and faults.

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In this paper, a model-based fault diagnosis methodology for PEM fuel cell systems is presented. The methodology is based on computing residuals using a LPV observer. Fault detection is based on using adaptive threshold generated using an interval observer. Fault isolation is performed using the Euclidean distance between observed relative residuals and theoretical relative sensitivities. To illustrate the results, the commercial fuel cell Ballard Nexac is used in simulation where a set of typical fault scenarios have been considered. Finally, the diagnosis results corresponding to those fault scenarios are presented. It is remarkable that with this methodology it is possible to diagnose and isolate all the considered faults in contrast with other well known methodologies which use the classic binary signature matrix approach.Postprint (author’s final draft

No abstract

Abstract-In this paper, a model-based fault diagnosis methodology for PEM fuel cell systems is presented. The methodology is based on computing residuals using an LPV observer. Sensor fault detection faces the problem of robustness using adaptive thresholds generated with interval observer. Fault isolation is performed using the Euclidean distance between the observed relative residuals and theoretical relative sensitivities. To illustrate the results, a commercial fuel cell Ballard Nexa c is used in simulation where a set of typical fault scenarios have been considered. Finally, the diagnosis results corresponding to those fault scenarios are presented. It is remarkable that with this methodology it is possible to diagnose all the considered faults in contrast with other well known methodologies which use the classic binary signature matrix approach.