Prognostics-enabled technologies have emerged over the last few years primarily for predictive maintenance activites such as condition based maintenance (CBM), or its successor, CBM+, that accounts for the entire network of support elements required to execute a CBM program. However, due to the challenges that arise from real-world systems and safety concerns, they have not been adopted for operational decision making based on system end of life estimates. It is typically cost-prohibitive or highly unsafe to run a system to complete failure and, therefore, engineers turn to simulation studies for analyzing system performance. Prognostics research has matured to a point where we can start putting pieces together to be deployed on real systems, but this reveals new problems. First, a lack of standardization exists within this body of research that hinders our ability to compose various technologies or study their joint interactions when used together. The second hindrance lies in data management and creates hurdles when trying to reproduce results for validation or use the data as input to machine learning algorithms. We propose an end-to-end object-oriented data management framework & simulation testbed that can be used for a wide variety of applications. We describe the requirements, design, and implementation of the framework and provide a use-case application involving a stochastic data collection experiment that demonstrates how the framework can be used.
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prognostics, software in the loop, testbed, UAV
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