Predictive maintenance management using sensor-based degradation models
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Expert Systems with Applications: An International Journal
Model-based and data-driven prognosis of automotive and electronic systems
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
Maintaining the health of software monitors
Innovations in Systems and Software Engineering
Fault diagnosis of electric railway traction substation with model-based relation guiding algorithm
Expert Systems with Applications: An International Journal
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Conventional maintenance strategies, such as corrective and preventive maintenance, are not adequate to fulfill the needs of expensive and high availability transportation and industrial systems. A new strategy based on forecasting system degradation through a prognostic process is required. The recent advances in model-based design technology have realized significant time savings in product development cycle. These advances facilitate the integration of model-based diagnosis and prognosis of systems, leading to condition-based maintenance and increased availability of systems. With an accurate simulation model of a system, diagnostics and prognostics can be synthesized concurrently with system design. In this paper, we develop an integrated prognostic process based on data collected from model-based simulations under nominal and degraded conditions. Prognostic models are constructed based on different random load conditions (modes). An interacting multiple model (IMM) is used to track the hidden damage. Remaining-life prediction is performed by mixing mode-based life predictions via time-averaged mode probabilities. The solution has the potential to be applicable to a variety of systems, ranging from automobiles to aerospace systems.