Gas Turbine Fault Diagnosis From Fast Response Data Using Probabilistic Methods and Information Fusion

Abstract

The paper covers firstly the use of probabilistic neural networks for the classification of spectral fault signatures obtained from fast response data (sound, vibration, unsteady pressure). The method is compared to other alternatives, such as geometrical and statistical pattern recognition. The effectiveness of the method is demonstrated by presenting the results from application to data from a radial compressor and an industrial gas turbine. Further, probabilistic methods are used to perform information fusion. The outcomes of different diagnostic methods are used as a first level of diagnostic inference, and are fed to two different fusion processes which are based on i) Probabilistic Neural Networks and ii) Bayesian Belief Networks. It is demonstrated that these fusion processes provide powerful tools for effective fault classification.