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Title: Analytical and knowledge-based redundancy for fault diagnosis in process plants

Abstract

The increasing complexity of process plants and their reliability have necessitated the development of more powerful methods for detecting and diagnosing process abnormalities. Among the underlying strategies, analytical redundancy and knowledge-based system techniques offer viable solutions. In this work, the authors consider the adaptive inclusion of analytical redundancy models (state and parameter estimation modules) in the diagnostic reasoning loop of a knowledge-based system. This helps overcome the difficulties associated with each category. The design method is a new layered knowledge base that houses compiled/qualitative knowledge in the high levels and process-general estimation knowledge in the low levels of a hierarchical knowledge structure. The compiled knowledge is used to narrow the diagnostic search space and provide an effective way of employing estimation modules. The estimation-based methods that resort to fundamental analysis provide the rationale for a qualitatively-guided reasoning process. The overall structure of the fault detection and isolation system based on the combined strategy is discussed focusing on the model-based redundancy methods which create the low levels of the hierarchical knowledge base. The system has been implemented using the condensate-feedwater subsystem of a coal-fired power plant. Due to the highly nonlinear and mixed-mode nature of the power plant dynamics, the modifiedmore » extended Kalman filter is used in designing local detection filters.« less

Authors:
;  [1];  [2]
  1. (Univ. of Colorado, Boulder (United States))
  2. (Higher Coll. of Engineering, Gora (Poland))
Publication Date:
OSTI Identifier:
6359601
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIChE Journal (American Institute of Chemical Engineers); (United States); Journal Volume: 39:1
Country of Publication:
United States
Language:
English
Subject:
20 FOSSIL-FUELED POWER PLANTS; FOSSIL-FUEL POWER PLANTS; DIAGNOSTIC TECHNIQUES; COAL; CONDENSATES; FAULT TREE ANALYSIS; FEEDWATER; KNOWLEDGE BASE; SAFETY ENGINEERING; CARBONACEOUS MATERIALS; ENERGY SOURCES; ENGINEERING; FOSSIL FUELS; FUELS; HYDROGEN COMPOUNDS; MATERIALS; OXYGEN COMPOUNDS; POWER PLANTS; SYSTEM FAILURE ANALYSIS; SYSTEMS ANALYSIS; THERMAL POWER PLANTS; WATER; 200101* - Fossil-Fueled Power Plants- Cooling & Heat Transfer Equipment & Systems

Citation Formats

Fathi, Z., Ramirez, W.F., and Korbicz, J. Analytical and knowledge-based redundancy for fault diagnosis in process plants. United States: N. p., 1993. Web. doi:10.1002/aic.690390107.
Fathi, Z., Ramirez, W.F., & Korbicz, J. Analytical and knowledge-based redundancy for fault diagnosis in process plants. United States. doi:10.1002/aic.690390107.
Fathi, Z., Ramirez, W.F., and Korbicz, J. 1993. "Analytical and knowledge-based redundancy for fault diagnosis in process plants". United States. doi:10.1002/aic.690390107.
@article{osti_6359601,
title = {Analytical and knowledge-based redundancy for fault diagnosis in process plants},
author = {Fathi, Z. and Ramirez, W.F. and Korbicz, J.},
abstractNote = {The increasing complexity of process plants and their reliability have necessitated the development of more powerful methods for detecting and diagnosing process abnormalities. Among the underlying strategies, analytical redundancy and knowledge-based system techniques offer viable solutions. In this work, the authors consider the adaptive inclusion of analytical redundancy models (state and parameter estimation modules) in the diagnostic reasoning loop of a knowledge-based system. This helps overcome the difficulties associated with each category. The design method is a new layered knowledge base that houses compiled/qualitative knowledge in the high levels and process-general estimation knowledge in the low levels of a hierarchical knowledge structure. The compiled knowledge is used to narrow the diagnostic search space and provide an effective way of employing estimation modules. The estimation-based methods that resort to fundamental analysis provide the rationale for a qualitatively-guided reasoning process. The overall structure of the fault detection and isolation system based on the combined strategy is discussed focusing on the model-based redundancy methods which create the low levels of the hierarchical knowledge base. The system has been implemented using the condensate-feedwater subsystem of a coal-fired power plant. Due to the highly nonlinear and mixed-mode nature of the power plant dynamics, the modified extended Kalman filter is used in designing local detection filters.},
doi = {10.1002/aic.690390107},
journal = {AIChE Journal (American Institute of Chemical Engineers); (United States)},
number = ,
volume = 39:1,
place = {United States},
year = 1993,
month = 1
}
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  • In order to remain efficiently functioning, chemical factories make heavy use of automated systems, such as warning systems and instrumentations, to monitor process variables and to control deviations within an allowable range in production processes. A process abnormality occurs when process variables (such as temperature/pressure) or process parameters (such as catalyst activity) deviate from the designed allowable ranges. A new model graph called fuzzy cause-effect digraph (FCDG) is proposed. This model expresses quantitative deviations of variables from the normal values with fuzzy set. It uses dynamic constraints (confluences) which are converted to dynamic fuzzy relations to express the dynamic gainmore » between the variables in a chemical process. This replaces the steady-state gain between the variables originally expressed with a +, {minus}, or 0 by signed directed graph (SDG). Using this FCDG model would eliminate spurious interpretations attributed to system compensations and inverse responses from backward loops and forward paths in the process. The basic idea and development of this proposed methods are described in this paper. Moreover, this method can apply fuzzy reasoning to estimate the states of the unmeasured variables, to explain fault propagation paths, and to ascertain fault origins. The algorithm of fault diagnosis and its application proposed in this paper are described in part 2.« less