Diagnosing Faults in Electrical Power Systems of Spacecraft and Aircraft
Electrical power systems play a critical role in spacecraft
and aircraft, and they exhibit a rich variety of failure modes.
This paper discusses electrical power system fault diagnosis
by means of probabilistic techniques. Specically, we discuss
our development of a diagnostic capability for an electrical
power system testbed, ADAPT, located at NASA Ames.
We emphasize how we have tackled two challenges, regarding
modelling and real-time performance, often encountered
when developing diagnostic applications. We carefully discuss
our Bayesian network modeling approach for electrical
power systems. To achieve real-time performance, we build
on recent theoretically well-founded developments that compile
a Bayesian network into an arithmetic circuit. Arithmetic
circuits have low footprint and are optimized for embedded,
real-time systems such as spacecraft and aircraft.
We discuss our probabilistic diagnostic models developed for
ADAPT along with successful experimental results.
Resources
1 resource available
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2008AAAI_Mengshoel_DiagnosingFaultsElectricalPowerSystems.pdf
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| accrualPeriodicity | irregular |
| bureauCode |
[ "026:00" ] |
| contactPoint |
{ "fn": "SCOTT POLL", "@type": "vcard:Contact", "hasEmail": "mailto:scott.d.poll@nasa.gov" } |
| description | Electrical power systems play a critical role in spacecraft and aircraft, and they exhibit a rich variety of failure modes. This paper discusses electrical power system fault diagnosis by means of probabilistic techniques. Specically, we discuss our development of a diagnostic capability for an electrical power system testbed, ADAPT, located at NASA Ames. We emphasize how we have tackled two challenges, regarding modelling and real-time performance, often encountered when developing diagnostic applications. We carefully discuss our Bayesian network modeling approach for electrical power systems. To achieve real-time performance, we build on recent theoretically well-founded developments that compile a Bayesian network into an arithmetic circuit. Arithmetic circuits have low footprint and are optimized for embedded, real-time systems such as spacecraft and aircraft. We discuss our probabilistic diagnostic models developed for ADAPT along with successful experimental results. |
| distribution |
[ { "@type": "dcat:Distribution", "title": "2008AAAI_Mengshoel_DiagnosingFaultsElectricalPowerSystems.pdf", "format": "PDF", "mediaType": "application/pdf", "description": "2008AAAI_Mengshoel_DiagnosingFaultsElectricalPowerSystems.pdf", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/2008AAAI_Mengshoel_DiagnosingFaultsElectricalPowerSystems.pdf" } ] |
| identifier | DASHLINK_865 |
| issued | 2013-12-18 |
| keyword |
[ "ames", "dashlink", "nasa" ] |
| landingPage | https://c3.nasa.gov/dashlink/resources/865/ |
| modified | 2025-03-31 |
| programCode |
[ "026:029" ] |
| publisher |
{ "name": "Dashlink", "@type": "org:Organization" } |
| title | Diagnosing Faults in Electrical Power Systems of Spacecraft and Aircraft |
