Probabilistic Fault Diagnosis in Electrical Power Systems

Metadata Updated: July 17, 2020

Electrical power systems play a critical role in spacecraft and aircraft. This paper discusses our development of a diagnostic capability for an electrical power system testbed, ADAPT, using probabilistic techniques. In the context of ADAPT, we present two challenges, regarding modelling and real-time performance, often encountered in real-world diagnostic applications. To meet the modelling challenge, we discuss our novel high-level specification language which supports auto-generation of Bayesian networks. To meet the real-time challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuits typically have small footprints and are optimized for the real-time avionics systems found in spacecraft and aircraft. Using our approach, we present how Bayesian networks with over 400 nodes are auto-generated and then compiled into arithmetic circuits. Using real-world data from ADAPT as well as simulated data, we obtain average inference times smaller than one millisecond when computing diagnostic queries using arithmetic circuits that model our real-world electrical power system.


O. J. Mengshoel, A. Darwiche, K. Cascio, M. Chavira, S. Poll, and S. Uckun, “Diagnosing Faults in Electrical Power Systems of Spacecraft and Aircraft”, In Proc. of the Twentieth Innovative Applications of Artificial Intelligence, Conference (IAAI-08), Chicago, IL, 2008.

BibTex Reference:

@inproceedings{mengshoel08diagnosing, author = {Mengshoel, O. J. and Darwiche, A. and Cascio, K. and Chavira, M. and Poll, S. and Uckun, S.}, title = {Diagnosing Faults in Electrical Power Systems of Spacecraft and Aircraft}, booktitle = {Proceedings of the Twentieth Innovative Applications of Artificial Intelligence Conference (IAAI-08)}, pages = {1699--1705}, address = {Chicago, IL}, year = {2008} }

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Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Created Date August 1, 2018
Metadata Updated Date July 17, 2020
Data Update Frequency irregular

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Harvested from NASA Data.json

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Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date July 17, 2020
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Data Update Frequency irregular
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Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2010-09-09
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Data Last Modified 2020-01-29
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