Application of Preprocessed Classifier Type Neural Network for Searching of Faulty Components of Power Cycles in Case of Incomplete Measurement Data

Abstract

ABSTRACT Thermal and flow diagnostics of power units makes use of diagnostic relations i.e. relations between fault signatures (sets of symptoms) and geometry degradation of its components. Determining symptoms may base on thorough thermal measurements of the cycle. However, numerous apparatuses in the cycle are not or cannot be properly equipped for necessary measurements. Examples of such apparatuses in a steam turbine are external glands and nozzle box sealings. The paper studies the applicability of a selected type of Artificial Neural Network, ANN, as a diagnostic relation for locating faulty apparatuses in HP and IP turbine casings, including their sealing systems. The obtained results can be assessed as good for single faults, and satisfactory for multiple faults of the cycle components. The examined type of ANN can be used e.g. in a modular hierarchical diagnostic system proposed by Gluch & Krzyzanowski, 199