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Automatic Expert Process Fault Diagnosis and Supervision

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Real Time Fault Monitoring of Industrial Processes

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Abstract

Automatic fault diagnosis, supervision and control of very complex systems are becoming extrememly important. This is the direct consequence of the occurrence of recent disasters because of unsatisfactory control or missed diagnosis of failures (Three Mile Island and Tchernobyl are but a few examples). Control and fault diagnosis cannot be realized without a good methodology of modeling, i.e. representing the structure and behavior of the systems under consideration in the significant states of their operation. The conventional methods of large scale modeling require comprehensive knowledge about the system consisting of conforming elements (e.g. a set of ordinary differential equations), and no gaps in the knowledge are allowed. Complex physical systems (e.g. a nuclear power plant, chemical processes) contain several types of elements and processes (e.g. nuclear, mechanical, electrical, electronic, etc.) with different types of description and eventually gaps in the available knowledge. The purely numerical-mathematical approach of large scale systems modeling could not offer adequate methodology to solve the problems arising in this field, therefore, symbolic and artificial intelligence methods have been tried to obtain an adequate solution.

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  1. Department of Production Engineering and Management, Technical University of Crete, Chania, Greece

    A. D. Pouliezos

  2. Electronic Engineering and Computer Science Department, Technical University of Crete, Chania, Greece

    G. S. Stavrakakis

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  1. A. D. Pouliezos
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Pouliezos, A.D., Stavrakakis, G.S. (1994). Automatic Expert Process Fault Diagnosis and Supervision. In: Real Time Fault Monitoring of Industrial Processes. International Series on Microprocessor-Based and Intelligent Systems Engineering, vol 12. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8300-8_4

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  • DOI: https://doi.org/10.1007/978-94-015-8300-8_4

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