An Evaluation Method for Feature Selection in Proton Exchange Membrane Fuel Cell Fault Diagnosis

Considering the fact that various features can be used in proton exchange membrane fuel cell (PEMFC) fault diagnosis, while the lack of feature evaluation method brings great difficulty in selecting appropriate features at practical PEMFC applications, a generalized feature evaluation and selection method is urgently required in PEMFC fault diagnosis. This paper proposes a novel feature evaluation method, where feature discrimination capacity and robustness are evaluated. With the proposed method, features providing accurate and consistent diagnostic performance can be determined. In the study, features widely used in existing PEMFC fault diagnosis are utilized, which are extracted from either PEMFC voltage or multi-sensor signals, and their effectiveness in identifying faults at different PEMFC systems is investigated. Results demonstrate that with the proposed evaluation method, available features from various PEMFC test data can be ranked based on their diagnostic results. From the findings, appropriate features for PEMFC fault diagnosis can be determined. Moreover, early stage PEMFC faults can also be distinguished with high ranking features. This will be beneficial in practical PEMFC systems, where mitigation strategies can be taken to remove the effect due to early stage faults.