An Expert System for Diagnostics and Estimation of Steam Turbine Components’ Condition

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 expansion 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

Digital diagnostic complex for power turbine units equipment

The study is devoted to the problem formulation for creation a concept of digital complex for power turbine equipment diagnostics using a reference turbine model formed by a set of computational models of thermal and gas-dynamic processes, by diagnostic expert systems (probabilistic type and decision tree type), by functional standard characteristics and statistical models. As part of the study it is proposed to develop diagnostic methods and algorithms for characteristic malfunctions causes for various subsystems of power equipment, which will allow them to be detected at an early stage and to prevent unplanned (emergency) equipment shutdown. © 2019 Published under licence by IOP Publishing Ltd

Turbine Diagnostics: Algorithms Adaptation Problems

Enterprises of energy equipment and operational utilities set sights on diagnostic systems. This is necessary for state control and maintenance planning of steam turbines. It is useful for digitalization purposes too. So far, some mathematical systems are already used. Algorithms for flow part, heat expansion systems, control systems, vibration-based diagnostics and auxiliary equipment have already been designed. We have designed algorithms just in principle. We met difficulties adapting them for the PT-75/80-90 turbine. Firstly, we should connect them to a single interface. Secondly, adaptation should include features of the equipment, its state (if not new), even operating conditions. Diagnostic signs for each turbine are the most important. We define them based on the operational data. When adapting the algorithms, we reconsider the signs list. We also estimate its coefficients of importance again. This requires experts to study designs, calculations, and modelling. We also analyzed a large amount of operational data at various power plants. To define the state we use tests. Adapting is based on the modes of a specific power station. Following this strategy, we adapt general algorithms for various turbines. © 2020 WIT Press