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Health Management and Prognostics for Electric Aircraft PowertrainW and c Any air borne vehicle needs incorporating safety as key parameter of measure, and inclusion of autonomy raises the critical need for safety under autonomous operations. Management of faults and component degradation is key as complexity in autonomous operations grow over the period of time. Therefore, in addition to basic operational requirements, an autonomous electric vehicle should be able to make accurate estimates of its current system health and take the correct decisions to complete its mission successfully. Real-time safety and state-awareness tools are therefore essential for the vehicle to be able to reach its destination in a safe and successful manner. The need for safety assurance and health management capabilities is particularly relevant for aircraft electric propulsion systems, which are relatively new and with limited historical to learn. They are critical systems requiring high power density along with reliability, resilience, efficient management of weight, and operational costs. A model- based fault diagnosis and prognostics approach of complex critical systems can successfully accomplish the safety and state awareness goal for such electric propulsion systems, enabling autonomous decision making capability for safe and efficient operation. To identify critical components in the system a Qualitative Bayesian approach using FMECA is implemented. This requires the assessment of some quantities representing the state of the electric unmanned aerial systems (e-UAS), as well as look-ahead forecasts of such states during the entire flight, presented in form of safety metrics (SM). In-service data and performance data gathered from degraded components sup- ports diagnostic and prognostic methods for these systems, but this data can be difficult to obtain as weight and packaging restrictions reduce redundancy and instrumentation on-board the vehicle. Therefore, an model-based framework should be capable or operating with limited data. In addition to data scarcity, the variability of such complex critical systems re- quires the model-based framework to reason in the presence of uncertainty, such as sensor noise, and modeling imperfections. Quantification of errors and uncertainties in the measured states and quantities is therefore a fundamental step for a precise estimation of such SMs; un-modeled uncertainty may result in erroneous state assessment and un- reliable predictions of future states of e-UAVs. Typical, centralized model-based schemes suffer from inherent disadvantages such as computational complexity, single point of failure, and scalability issues, and therefore may fail in such a complex scenario. This paper presents a methodology for developing a system level diagnostics and prognostics approach using a Qualitative Bayesian FMECA approach along with a formal uncertainty management framework for an e-UAS. In this work we demonstrate the efficacy of the framework to predict effects of sub-system level degradation on vehicle operation incorporating uncertainty management to predict future behavior under different operating conditions.
Document ID
20190032482
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Kulkarni, Chetan
(Stinger Ghaffarian Technologies Inc. (SGT Inc.) Moffett Field, CA, United States)
Corbetta, Matteo
(Stinger Ghaffarian Technologies Inc. (SGT Inc.) Moffett Field, CA, United States)
Date Acquired
November 4, 2019
Publication Date
August 21, 2019
Subject Category
Life Sciences (General)
Report/Patent Number
ARC-E-DAA-TN72593-1
Meeting Information
Meeting: Electric Aircraft Technical Symposium
Location: Indianpolis, IN
Country: United States
Start Date: August 21, 2019
End Date: August 23, 2019
Sponsors: American Institute of Aeronautics and Astronautics (AIAA)
Funding Number(s)
CONTRACT_GRANT: NNA14AA60C
Distribution Limits
Public
Copyright
Public Use Permitted.
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