Time-Domain Self-Clustering-Based Diagnosis Applied on Open Cathode Fuel Cell

Abstract

The ability of a diagnosis tool to observe an abnormal state of a system remains a major issue for health monitoring. For that purpose, several diagnosis tools have been proposed in the literature. Most of them are developed for specific system characterization, and the genericity of the approaches is not considered. Indeed, most approaches proposed in the literature are based on an expert offline consideration that makes it hard to apply the strategy to other systems. It is therefore important to develop a diagnostic tool that takes as little as possible expert knowledge to reduce the dependency between the tool and the system. This paper, therefore, focuses on the application of a generic diagnosis tool on an open cathode fuel cell. The goal is to feed the diagnosis algorithm with a voltage measurement and let it proceed to a self-clustering of the signal components. Each cluster’s interpretation remains to be established by the expert point of view that is then involved downstream of the diagnosis tool.