An expert system for diagnostics and estimation of steam turbine components’ condition

An expert system for diagnostics and estimation of steam turbine components’ condition

Konstantin E. Aronson Boris E. Murmansky Ilia B. Murmanskii Yuri M. Brodov


Ural Federal University named after the First President of Russia B.N. Yeltsin, Ekaterinburg, Russia

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This article describes an expert system of probability type for diagnostics and state estimation of steam turbine technological subsystems’ components. The expert system is based on Bayes’ theorem and permits one to troubleshoot the equipment components, using expert experience, when there is a lack of baseline information on the indicators of turbine operation. Within a unified approach, the expert system solves the problems of diagnosing the flow steam path of the turbine, bearings, thermal expan- sion system, regulatory system, condensing unit, and the systems of regenerative feed-water and hot water heating. The knowledge base of the expert system for turbine unit rotors and bearings contains a description of 34 defects and 104 related diagnostic features that cause a change in its vibration state. The knowledge base for the condensing unit contains 12 hypotheses and 15 pieces of evidence (indications); the procedures are also designated for 20 state parameters’ estimation. Similar knowledge bases containing the diagnostic features and fault hypotheses are formulated for other technological subsystems of a turbine unit. With the necessary initial information available, a number of problems can be solved within the expert system for various technological subsystems of steam turbine unit: for steam flow path, it is the correlation and regression analysis of multifactor relationship between the vibration and the regime parameters; for thermal expansion system, it is the evaluation of force acting on the longitudinal keys depending on the temperature state of the turbine cylinder; for condensing unit, it is the evaluation of separate effect of the heat exchange surface contamination and of the presence of air in condenser steam space on condenser thermal efficiency performance, as well as the evaluation of term for condenser cleaning and for tube system replacement. With the lack of initial information, the expert system formulates a diagnosis and calculates the probability of faults’ origin.


diagnostic, diagnostic features, expert system, evidence, faults, hypotheses, steam turbine


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