Since gas turbines are very complex and potentially unreliable machines, the improvement of their monitoring systems becomes an essential part. Considering this necessity, the present paper performs a gas turbine diagnostic algorithm testing. The methodology proposed is formed by three stages. In the first stage, the commercial software ProDiMES (Propulsion Diagnostic Method Evaluation Strategy) is used to simulate an engine fleet and generate data with fault and no-fault conditions. In the second stage, a baseline model testing is implemented to improve the healthy engine performance approximation. Finally, a fault recognition stage based on a pattern recognition technique (Multi-Layer Perceptron) performs the diagnosis and calculates the probability of correct diagnostic decisions. The results obtained show that: a) the software ProDiMES is an easy and convenient tool to evaluate gas turbine diagnostic methods, b) the baseline model testing is a key step because it allows reducing the errors that can negatively influence the diagnostic process and c) the algorithm correctly performs the fault recognition task.
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