Gas turbines are high value industrial assets with significant role in various kinds of industrial processes, health management systems are therefore important for helping maintaining gas turbines’ stability in long-term operations. With more and more performance data able to be collected by sensors and the new machine learning methods developed, researchers are able to build more powerful digital models to monitor the gas turbine.

This paper introduces a performance parameters dynamic alarm scheme for gas turbine. The proposed scheme contains 3 parts: Part 1, a dynamic multi-part artificial neural network trained using real performance data that can simulate different parts of gas turbine and output “normal” sensor values to make comparison with the actual value collected; Part 2, a group of thresholds set according to system noise that detects sudden failures by sensing abnormal performance values, this also decides which data should be used to update the neural network; Part3, a recorder for “benchmark point” outputs that can reflect change of the gas turbine’s status and detect long-term degradation.

Unlike traditional approaches, the proposed dynamic method separates long term degradation and short term sudden failure, therefore both faults can be detected accurately. The core of the proposed method is that physical properties are embedded into the neural network to regulate training and make the model more interpretable.  In our scheme, a gas turbine is divided into 4 parts referencing the equipment’s physical mechanism, they are simulated digitally by 4 sub corresponding networks, which are then combined into the proposed integrated network. The proposed scheme achieves an overall pleasing result.


Dynamic Gas Turbine Condition Monitoring Scheme With Multi-Part Neural Network

Division / Sponsor:
International Gas Turbine Institute (IGTI)

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2020/volume/84058

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