PRODIAG: Combined expert system/neural network for process fault diagnosis. Volume 1, Theory

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The function of the PRODIAG code is to diagnose on-line the root cause of a thermal-hydraulic (T-H) system transient with trace back to the identification of the malfunctioning component using the T-H instrumentation signals exclusively. The code methodology is based on the Al techniques of automated reasoning/expert systems (ES) and artificial neural networks (ANN). The research and development objective is to develop a generic code methodology which would be plant- and T-H-system-independent. For the ES part the only plant or T-H system specific code requirements would be implemented through input only and at that only through a Piping and Instrumentation ... continued below

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187 p.

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Reifman, J.; Wei, T.Y.C. & Vitela, J.E. September 1, 1995.

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Description

The function of the PRODIAG code is to diagnose on-line the root cause of a thermal-hydraulic (T-H) system transient with trace back to the identification of the malfunctioning component using the T-H instrumentation signals exclusively. The code methodology is based on the Al techniques of automated reasoning/expert systems (ES) and artificial neural networks (ANN). The research and development objective is to develop a generic code methodology which would be plant- and T-H-system-independent. For the ES part the only plant or T-H system specific code requirements would be implemented through input only and at that only through a Piping and Instrumentation Diagram (PID) database. For the ANN part the only plant or T-H system specific code requirements would be through the ANN training data for normal component characteristics and the same PID database information. PRODIAG would, therefore, be generic and portable from T-H system to T-H system and from plant to plant without requiring any code-related modifications except for the PID database and the ANN training with the normal component characteristics. This would give PRODIAG the generic feature which numerical simulation plant codes such as TRAC or RELAP5 have. As the code is applied to different plants and different T-H systems, only the connectivity information, the operating conditions and the normal component characteristics are changed, and the changes are made entirely through input. Verification and validation of PRODIAG would, be T-H system independent and would be performed only ``once``.

Physical Description

187 p.

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OSTI as DE96008333

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  • Other Information: PBD: Sep 1995

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  • Other: DE96008333
  • Report No.: ANL/RE/RP--89482-Vol.1
  • Grant Number: W-31109-ENG-38
  • DOI: 10.2172/219266 | External Link
  • Office of Scientific & Technical Information Report Number: 219266
  • Archival Resource Key: ark:/67531/metadc671309

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  • September 1, 1995

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  • June 29, 2015, 9:42 p.m.

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  • Dec. 16, 2015, 5:36 p.m.

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Reifman, J.; Wei, T.Y.C. & Vitela, J.E. PRODIAG: Combined expert system/neural network for process fault diagnosis. Volume 1, Theory, report, September 1, 1995; Illinois. (digital.library.unt.edu/ark:/67531/metadc671309/: accessed September 22, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.