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Systems Health Monitoring: Integrating FMEA into Bayesian NetworksThe foreseeable high traffic density suggests that a large number of electric propulsion systems will enter the airspace, and that they will also operate at high frequency, e.g., large number of take offs and landings per unit time. The reliability of such critical systems is therefore key to ensure high safety standards in the low-altitude airspace. Diagnostic systems, which aim at identifying incipient faults, can mitigate unexpected failures or lower-than-expected reliability by performing early fault detection by monitoring the systems. A key element of fault diagnosis is fault detection and isolation (FDI), which complexity increases with the complexity of the system itself, namely the number of subsystems and components, interactions among sub-systems, and the number of sensors available. The proposed approach leverages combination of failure mode and effect analysis (FMEA) integrated with Bayesian networks, thus introducing dependability structures into a diagnostic framework to aid FDI. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded within FMEAs.

The integrated framework enables the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. In this work, sub systems of Urban Air Mobility (UAM) type vehicle like avionics, structures, power-train etc. are taken into account to show the approach at the system level. This work integrates early design phase in the development of UAM type vehicles with diagnostic tools, which are often developed later in the product life-cycle, or retrofitted at a later time on systems. Failure mode and effect analysis (FMEA) derived for the system in the design phase is embedded within a Bayesian network (BN).
Document ID
20210000491
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Chetan S Kulkarni
(KBR (United States) Houston, Texas, United States)
Matteo Corbetta
(KBR (United States) Houston, Texas, United States)
Elinirina I Robinson
(KBR (United States) Houston, Texas, United States)
Date Acquired
January 15, 2021
Subject Category
Air Transportation and Safety
Report/Patent Number
20205006373
Meeting Information
Meeting: 42nd International IEEE Aerospace Conference
Location: Big Sky, MO
Country: US
Start Date: March 6, 2021
End Date: March 13, 2021
Sponsors: Institute of Electrical and Electronics Engineers, American Institute for Aeronautics & Astronautics, Prognostics and Health Management Society
Funding Number(s)
CONTRACT_GRANT: 80ARC020D0010
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
NASA Peer Committee
Keywords
UAM
Bayesian Networks
FMEA
Systems Health
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