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Analysis of Gas Turbine Compressor Performance after a Major Maintenance Operation Using an Autoencoder Architecture.

Martí de Castro-Cros1, Manel Velasco1, Cecilio Angulo1

  • 1Intelligent Data Science and Artificial Intelligence Research Centre (IDEAI), Automatic Control Department, Universitat Politècnica de Catalunya, Campus Nord, Carrer de Jordi Girona, 1, 3, 08034 Barcelona, Spain.

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Summary
This summary is machine-generated.

Autoencoders effectively analyzed industrial gas turbine (IGT) compressor performance post-maintenance. This machine learning approach helps update operational models and indicates long-term compressor behavior for better maintenance strategies.

Keywords:
artificial intelligenceautoencodercompressorcondition assessmentgas turbine

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Area of Science:

  • Engineering
  • Data Science
  • Machine Learning

Background:

  • Industrial gas turbines (IGTs) require significant investment in maintenance.
  • Machine learning and data availability are transforming industrial decision-making.
  • Monitoring and predicting difficult-to-measure process variables are crucial for industrial operations.

Purpose of the Study:

  • To analyze the performance of an industrial gas turbine (IGT) compressor after major maintenance using autoencoder models.
  • To assess the effectiveness of autoencoder-based feature extraction for condition monitoring.
  • To identify changes in the IGT compressor's operational point and inform model updates.

Main Methods:

  • Utilized two variations of autoencoder algorithms for data analysis.
  • Employed sensor data from the compressor and ambient factors for condition assessment.
  • Analyzed changes in the IGT's operational point post-maintenance.

Main Results:

  • Significant changes in the IGT compressor's operation point were detected after major maintenance.
  • Autoencoder models proved effective in feature extraction for condition assessment.
  • The autoencoder approach can define an indicator for long-term compressor behavior.

Conclusions:

  • The study highlights the need to update internal operating models following major maintenance due to altered operational modes.
  • Autoencoder-based models are effective tools for analyzing IGT performance and extracting relevant features.
  • This methodology provides a valuable indicator for monitoring long-term compressor behavior and optimizing maintenance.