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  • 1
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    Dynamic Adjustment of Simulation Parameters for Efficient Vehicle Prognostics (2018)
    In:  CASI
    Details
    Publication Date: 2019-07-19
    Description: Model-based diagnostics and prognostics rely on state estimation and uncertainty management algorithms to produce useful information for system operators and maintainers. This information enables more informed operational decisions, condition-based maintenance, and overall mission safety assurance. Typically, uncertainty is associated with vehicle state-of-health estimation and prediction results because of modeling errors, internal or external sources of noise, and sensor inaccuracy. Probabilistic uncertainty management methods including Sequential Monte Carlo simulation are commonly used to reason about state-of-health estimates and predictions in the presence of these sources of uncertainty. However, such algorithms can be computationally expensive as they require a very large number of samples to obtain a sufficiently accurate quantification of the end of life probability distribution. As a result, highly mobile autonomous systems that leverage the prognostic results for mission-level replanning are often constrained in their processing capability because of these computationally expensive simulation approaches. Therefore, in this paper, we investigate algorithmic methods for dynamically adjusting simulation time step as well as number of samples to achieve highly efficient prognostic results while maintaining results accuracy. Results obtained from simulated flight experiments of an electric unmanned aerial vehicle are presented to verify the efficacy of such algorithms.
    Keywords: Electronics and Electrical Engineering
    Type: ARC-E-DAA-TN53265 , European Conference of the Prognostics and Health Management Society 2018; Jul 03, 2018 - Jul 06, 2018; Utrecht; Netherlands
    Format: application/pdf
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    NASA TECHNICAL REPORTS
  • 2
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    GPU Accelerated Prognostics (2017)
    In:  CASI
    Details
    Publication Date: 2019-07-13
    Description: Prognostic methods enable operators and maintainers to predict the future performance for critical systems. However, these methods can be computationally expensive and may need to be performed each time new information about the system becomes available. In light of these computational requirements, we have investigated the application of graphics processing units (GPUs) as a computational platform for real-time prognostics. Recent advances in GPU technology have reduced cost and increased the computational capability of these highly parallel processing units, making them more attractive for the deployment of prognostic software. We present a survey of model-based prognostic algorithms with considerations for leveraging the parallel architecture of the GPU and a case study of GPU-accelerated battery prognostics with computational performance results.
    Keywords: Computer Systems
    Type: ARC-E-DAA-TN46389 , Annual Conference of the Prognostics and Health Management Society 2017; Oct 02, 2017 - Oct 05, 2017; Saint Petersburg, FL; United States
    Format: application/pdf
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    NASA TECHNICAL REPORTS
  • 3
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    A Novel UAV Electric Propulsion Testbed for Diagnostics and Prognostics (2017)
    In:  CASI
    Details
    Publication Date: 2019-07-13
    Description: This paper presents a novel hardware-in-the-loop (HIL) testbed for systems level diagnostics and prognostics of an electric propulsion system used in UAVs (unmanned aerial vehicle). Referencing the all electric, Edge 540T aircraft used in science and research by NASA Langley Flight Research Center, the HIL testbed includes an identical propulsion system, consisting of motors, speed controllers and batteries. Isolated under a controlled laboratory environment, the propulsion system has been instrumented for advanced diagnostics and prognostics. To produce flight like loading on the system a slave motor is coupled to the motor under test (MUT) and provides variable mechanical resistance, and the capability of introducing nondestructive mechanical wear-like frictional loads on the system. This testbed enables the verification of mathematical models of each component of the propulsion system, the repeatable generation of flight-like loads on the system for fault analysis, test-to-failure scenarios, and the development of advanced system level diagnostics and prognostics methods. The capabilities of the testbed are extended through the integration of a LabVIEW-based client for the Live Virtual Constructive Distributed Environment (LVCDC) Gateway which enables both the publishing of generated data for remotely located observers and prognosers and the synchronization the testbed propulsion system with vehicles in the air. The developed HIL testbed gives researchers easy access to a scientifically relevant portion of the aircraft without the overhead and dangers encountered during actual flight.
    Keywords: Aircraft Design, Testing and Performance
    Type: ARC-E-DAA-TN45143 , 2017 IEEE AUTOTESTCON; Sep 11, 2017 - Sep 14, 2017; Schaumburg, IL; United States
    Format: application/pdf
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