A data-driven strategy for characterizing the water management failure in a Proton Exchange Membrane Fuel Cell (PEMFC) is presented in this paper. To carry out the diagnosis of water management failure, first the original single cell voltages are projected into lower-dimension features by applying orthogonal linear discriminant analysis (OLDA). Then, a classification methodology termed relevance vector machine (RVM) is employed to classify the lower-dimension features into different categories that indicate the respective health states of the system. The initially trained projecting vectors and classifiers lose their efficiency gradually the characteristics of PEMFC system change, such as the cell voltages decaying with time due to the normal degradation due to aging. An online adaptive diagnostic strategy based on the posterior probability of RVM is proposed, so as to keep the diagnostic accuracy over time. The efficiency and reliability of this online adaptive diagnostic strategy is validated using an experimental database from a 90-cell PEMFC stack. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.