A novel dynamic radius support vector data description based fault diagnosis method for proton exchange membrane fuel cell systems.

Timely fault detection is critical to improving the reliability and durability of the proton exchange membrane fuel cell (PEMFC) system. This paper proposes a novel fault diagnosis method, dynamic radius support vector data description (DR-SVDD), to efficiently identify the PEMFC system's faults. Compared to the classic support vector data description (SVDD) and improved SVDDs, this method considers both the SVDD hypersphere radius information and the distribution characteristics of the training set samples to obtain a more accurate and adequate description of the sample data. The cell voltages and the pressure drops at the cathode and anode obtained experimentally under various fault conditions are chosen as the feature variables for the PEMFC fault diagnosis. The comparative results show that the proposed DR-SVDD strategy performs well in fault class identification for a PEMFC system. [Display omitted] • A novel dynamic radius support vector data description (DR-SVDD) method is developed. • Multi-class discriminant function is used to identify PEMFC multiple faults. • The proposed DR-SVDD method exhibits more excellent classification performance in PEMFC fault diagnosis. [ABSTRACT FROM AUTHOR]