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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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.\}",

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doi = "10.1109/TNS.2015.2440993",

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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

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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

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ER -

System Health Awareness in Total-Ionizing Dose Environments

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Keywords

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