This paper introduces a probabilistic modeling of alarm observation delay, and shows a novel method of model-based diagnosis for time series observation. First, a fault model is defined by associating an event tree rooted by each fault hypothesis with probabilistic variables representing temporal delay. The most probable hypothesis is obtained by selecting one whose Akaike information criterion (AIC) is minimal. It is proved by simulation that the AIC-based hypothesis selection achieves a high precision in diagnosis.
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Kazuo HASHIMOTO, Kazunori MATSUMOTO, Norio SHIRATORI, "A New Diagnostic Method Using Probabilistic Temporal Fault Models" in IEICE TRANSACTIONS on Information,
vol. E85-D, no. 3, pp. 444-454, March 2002, doi: .
Abstract: This paper introduces a probabilistic modeling of alarm observation delay, and shows a novel method of model-based diagnosis for time series observation. First, a fault model is defined by associating an event tree rooted by each fault hypothesis with probabilistic variables representing temporal delay. The most probable hypothesis is obtained by selecting one whose Akaike information criterion (AIC) is minimal. It is proved by simulation that the AIC-based hypothesis selection achieves a high precision in diagnosis.
URL: https://global.ieice.org/en_transactions/information/10.1587/e85-d_3_444/_p
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@ARTICLE{e85-d_3_444,
author={Kazuo HASHIMOTO, Kazunori MATSUMOTO, Norio SHIRATORI, },
journal={IEICE TRANSACTIONS on Information},
title={A New Diagnostic Method Using Probabilistic Temporal Fault Models},
year={2002},
volume={E85-D},
number={3},
pages={444-454},
abstract={This paper introduces a probabilistic modeling of alarm observation delay, and shows a novel method of model-based diagnosis for time series observation. First, a fault model is defined by associating an event tree rooted by each fault hypothesis with probabilistic variables representing temporal delay. The most probable hypothesis is obtained by selecting one whose Akaike information criterion (AIC) is minimal. It is proved by simulation that the AIC-based hypothesis selection achieves a high precision in diagnosis.},
keywords={},
doi={},
ISSN={},
month={March},}
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TY - JOUR
TI - A New Diagnostic Method Using Probabilistic Temporal Fault Models
T2 - IEICE TRANSACTIONS on Information
SP - 444
EP - 454
AU - Kazuo HASHIMOTO
AU - Kazunori MATSUMOTO
AU - Norio SHIRATORI
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E85-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2002
AB - This paper introduces a probabilistic modeling of alarm observation delay, and shows a novel method of model-based diagnosis for time series observation. First, a fault model is defined by associating an event tree rooted by each fault hypothesis with probabilistic variables representing temporal delay. The most probable hypothesis is obtained by selecting one whose Akaike information criterion (AIC) is minimal. It is proved by simulation that the AIC-based hypothesis selection achieves a high precision in diagnosis.
ER -