System Health Awareness in Total-Ionizing Dose Environments

Zachary J. Diggins; Nagabhushan Mahadevan; E. Bryn Pitt; Daniel Herbison; Gabor Karsai; Brian D. Sierawski; Eric J. Barth; Robert A. Reed; Ronald D. Schrimpf; Robert A. Weller; Michael L. Alles; Arthur F. Witulski

doi:10.1109/TNS.2015.2440993

System Health Awareness in Total-Ionizing Dose Environments

Zachary J. Diggins
, Nagabhushan Mahadevan
, E. Bryn Pitt
, Daniel Herbison
, Gabor Karsai
, Brian D. Sierawski
, Eric J. Barth
, Robert A. Reed
, Ronald D. Schrimpf
, Robert A. Weller
, Michael L. Alles
, Arthur F. Witulski

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

Understanding the relationship between the impact of radiation at the component and system levels is challenging. This paper discusses a hierarchical approach, based on Bayesian theory, to establish a mechanism for determining system health based on the status of, and interactions between, the radiation response of component parts. When the Bayesian network is trained with a combination of experimental data, data from similar parts, simulations, and expert estimates, a quantitative estimate of the Total-Ionizing Dose (TID) response of a system can be obtained. Bayesian networks enable inference about system-level functional performance, the dose exposure, and the sensitivity of different components to TID, thus providing a framework for TID awareness in design and operation of systems. A case study of a robotic system consisting of commercial components is presented.

Original language	English
Article number	7154519
Pages (from-to)	1674-1681
Number of pages	8
Journal	IEEE Transactions on Nuclear Science
Volume	62
Issue number	4
DOIs	https://doi.org/10.1109/TNS.2015.2440993
State	Published - Aug 1 2015
Externally published	Yes

Keywords

Bayesian network
Fukushima
commercial off the shelf (COTS)
nuclear power
radiation hardness assurance
robot
total-ionizing dose

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10.1109/TNS.2015.2440993

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title = "System Health Awareness in Total-Ionizing Dose Environments",

abstract = "Understanding the relationship between the impact of radiation at the component and system levels is challenging. This paper discusses a hierarchical approach, based on Bayesian theory, to establish a mechanism for determining system health based on the status of, and interactions between, the radiation response of component parts. When the Bayesian network is trained with a combination of experimental data, data from similar parts, simulations, and expert estimates, a quantitative estimate of the Total-Ionizing Dose (TID) response of a system can be obtained. Bayesian networks enable inference about system-level functional performance, the dose exposure, and the sensitivity of different components to TID, thus providing a framework for TID awareness in design and operation of systems. A case study of a robotic system consisting of commercial components is presented.",

keywords = "Bayesian network, Fukushima, commercial off the shelf (COTS), nuclear power, radiation hardness assurance, robot, total-ionizing dose",

author = "Diggins, \{Zachary J.\} and Nagabhushan Mahadevan and \{Bryn Pitt\}, E. and Daniel Herbison and Gabor Karsai and Sierawski, \{Brian D.\} and Barth, \{Eric J.\} and Reed, \{Robert A.\} and Schrimpf, \{Ronald D.\} and Weller, \{Robert A.\} and Alles, \{Michael L.\} and Witulski, \{Arthur F.\}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.",

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doi = "10.1109/TNS.2015.2440993",

language = "English",

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AU - Diggins, Zachary J.

AU - Mahadevan, Nagabhushan

AU - Bryn Pitt, E.

AU - Herbison, Daniel

AU - Karsai, Gabor

AU - Sierawski, Brian D.

AU - Barth, Eric J.

AU - Reed, Robert A.

AU - Schrimpf, Ronald D.

AU - Weller, Robert A.

AU - Alles, Michael L.

AU - Witulski, Arthur F.

PY - 2015/8/1

Y1 - 2015/8/1

N2 - Understanding the relationship between the impact of radiation at the component and system levels is challenging. This paper discusses a hierarchical approach, based on Bayesian theory, to establish a mechanism for determining system health based on the status of, and interactions between, the radiation response of component parts. When the Bayesian network is trained with a combination of experimental data, data from similar parts, simulations, and expert estimates, a quantitative estimate of the Total-Ionizing Dose (TID) response of a system can be obtained. Bayesian networks enable inference about system-level functional performance, the dose exposure, and the sensitivity of different components to TID, thus providing a framework for TID awareness in design and operation of systems. A case study of a robotic system consisting of commercial components is presented.

AB - Understanding the relationship between the impact of radiation at the component and system levels is challenging. This paper discusses a hierarchical approach, based on Bayesian theory, to establish a mechanism for determining system health based on the status of, and interactions between, the radiation response of component parts. When the Bayesian network is trained with a combination of experimental data, data from similar parts, simulations, and expert estimates, a quantitative estimate of the Total-Ionizing Dose (TID) response of a system can be obtained. Bayesian networks enable inference about system-level functional performance, the dose exposure, and the sensitivity of different components to TID, thus providing a framework for TID awareness in design and operation of systems. A case study of a robotic system consisting of commercial components is presented.

KW - Bayesian network

KW - Fukushima

KW - commercial off the shelf (COTS)

KW - nuclear power

KW - radiation hardness assurance

KW - robot

KW - total-ionizing dose

UR - https://www.scopus.com/pages/publications/85027932235

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JO - IEEE Transactions on Nuclear Science

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M1 - 7154519

ER -

System Health Awareness in Total-Ionizing Dose Environments

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Keywords

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