A Study Toward Appropriate Architecture of System-Level Prognostics: Physics-Based and Data-Driven Approaches

Abstract

Many existing studies have investigated component-level Prognostics and Health Management (PHM) problems. In the real field, the PHM for the system is more important, which deals with the exploration of the system health derived from components degradations, from which the decision can be made to which components to repair. While there have been few recent studies in this direction, no studies are found that have investigated this issue from the systems perspective. Motivated by this, appropriate architecture for the system-level PHM is proposed for the physics-based and data-driven approaches. The architecture is demonstrated using a direct current (DC) motor system, which addresses the system health by the degradation of two components: bearing and permanent magnet. Due to the lack of real field data, simulation data are made using the motor dynamic equation. The two approaches are compared from the perspective of model construction and required information. In conclusion, the proposed architecture enables the estimation of components and system health, as well as the prediction of their remaining useful life. Furthermore, a what-if study allows us to investigate how long the system can be operated by repairing each component, from which the optimum maintenance plan can be made.

Keywords

• Architectures
• system-level prognostics
• physics-based approach
• data-driven approach
• remaining useful life (RUL)
• DC motor system