Hierarchical fault classification for resource constrained systems

Abstract

Abstract Prognostics and health management (PHM) is the study of using health information to support decision making to improve maintenance and operations. There are many existing methods for PHM but most solely focus on predictive accuracy and ignore resource constraints. In a real-world application of a PHM system, resources consumed by the predictive algorithm at the core of the PHM system could be a limiting factor. In this study, we propose using a hierarchical classification scheme to break the conventional classification problem into many sub-problems arranged in a hierarchy. By splitting the diagnostic task into many sub-problems, the hierarchical classifier can be constructed to maximize accuracy while minimizing resource consumption. Reinforcement learning is proposed to select the classifiers for each sub-problem. The proposed methodology is applied to condition monitoring of a hydraulic actuator where power is a limiting resource. Numerical experiments demonstrate that the proposed hierarchical classification method can reduce resource consumption compared to a traditional flat classification approach.