skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: A hybrid model combining first-principles and data-driven models for on-line condition monitoring

Abstract

We present an online anomaly detection technique using a hybrid method which combines first-principles (physical) models with data-driven (empirical) models. We use an error propagation scheme for computing the output variance of the proposed hybrid model, which utilizes the input and output measurement errors along with modeling uncertainty. This on-line model output variance estimation technique is used in a statistical test to determine whether observed output measurements are statistically too distant from expected output values (for given inputs) as to declare that an anomaly has occurred. The performance of the proposed error-propagation approach for anomaly detection was successfully tested in a simulated experiment. (authors)

Authors:
 [1]; ;  [2]
  1. Nuclear Engineering and Radiological Sciences, Univ. of Michigan, Ann Arbor, MI 48109 (United States)
  2. Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415-6180 (United States)
Publication Date:
Research Org.:
American Nuclear Society, 555 North Kensington Avenue, La Grange Park, IL 60526 (United States)
OSTI Identifier:
22029984
Resource Type:
Conference
Resource Relation:
Conference: NPIC and HMIT 2006: 5. International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology, Albuquerque, NM (United States), 12-16 Nov 2006; Other Information: Country of input: France; 3 refs.; Related Information: In: Proceedings of the 5. International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology| 1430 p.
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICAL METHODS AND COMPUTING; COMPUTERIZED SIMULATION; DATA PROCESSING; DETECTION; ERRORS; MATHEMATICAL MODELS; MONITORING

Citation Formats

Alpay, B, Garcia, H E, and Yoo, T S. A hybrid model combining first-principles and data-driven models for on-line condition monitoring. United States: N. p., 2006. Web.
Alpay, B, Garcia, H E, & Yoo, T S. A hybrid model combining first-principles and data-driven models for on-line condition monitoring. United States.
Alpay, B, Garcia, H E, and Yoo, T S. Sat . "A hybrid model combining first-principles and data-driven models for on-line condition monitoring". United States.
@article{osti_22029984,
title = {A hybrid model combining first-principles and data-driven models for on-line condition monitoring},
author = {Alpay, B and Garcia, H E and Yoo, T S},
abstractNote = {We present an online anomaly detection technique using a hybrid method which combines first-principles (physical) models with data-driven (empirical) models. We use an error propagation scheme for computing the output variance of the proposed hybrid model, which utilizes the input and output measurement errors along with modeling uncertainty. This on-line model output variance estimation technique is used in a statistical test to determine whether observed output measurements are statistically too distant from expected output values (for given inputs) as to declare that an anomaly has occurred. The performance of the proposed error-propagation approach for anomaly detection was successfully tested in a simulated experiment. (authors)},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2006},
month = {7}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: