Failure diagnosis of rotating machines for steam turbine in Cap-Djinet thermal power plant

Abstract

The Cap-Djinet thermal power plant is a 1872-megawatt (MW) gas power plant located in Djinet in Algeria. The steam turbine is an important strategic machine in this plant. A malfunction of the turbine can generate a high maintenance and production cost due to the classical diagnosis method based on conditional maintenance. The important amount of information given by the different sensors keeps the diagnosis operation more difficult; it takes more time to be effective, resulting in an additional cost for failures. The present paper emphasises a new professional approach capable of diagnosing the different failures in real time. This approach is based on the combination of the Fault Tree (FT) method and the Artificial Neural Network (ANN) method. Only six failure modes have been considered in this study. Each theme has been represented by an integrated turbine sensor. The FT is used to model all the probable causes of the different failure modes. On the other hand, ANN is used to accelerate the diagnosis procedure by learning all the possible combinations of failures given by FT analysis. The ANN was tested using different failure scenarios. The learning and testing results were successfully completed and the proposed ANN was able to detect the failures. This proposed method can help the maintenance agent to find fastly, in real-time, the probable failure of the steam turbine. The precision and accuracy of this method have been validated using a one-hundred simulation test