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Related Experiment Video

Updated: Nov 7, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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Published on: October 28, 2022

Condition Assessment of Industrial Gas Turbine Compressor Using a Drift Soft Sensor Based in Autoencoder.

Martí de Castro-Cros¹, Stefano Rosso², Edgar Bahilo³

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

Sensors (Basel, Switzerland)

|April 30, 2021

Summary

This study developed a soft sensor using an autoencoder architecture to monitor the condition of an industrial gas turbine compressor. The soft sensor effectively tracks performance changes over five years, providing a qualitative indicator of compressor health.

Keywords:

artificial intelligence autoencoder condition assessment gas turbine soft sensor

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Last Updated: Nov 7, 2025

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Area of Science:

Engineering
Data Science

Background:

Industrial maintenance is evolving with increased data availability and advanced condition assessment techniques.
Soft sensors are increasingly utilized for real-time monitoring and prediction of industrial process variables.
Accurate condition monitoring is crucial for ensuring the reliability and availability of industrial systems like gas turbines.

Purpose of the Study:

To develop and evaluate a soft sensor for monitoring the condition of an industrial gas turbine compressor.
To assess the long-term performance and identify changes in compressor behavior over time.
To create a qualitative indicator for evaluating compressor health in industrial gas turbines.

Main Methods:

Utilized a five-year dataset from multiple sensors installed on the industrial gas turbine compressor.
Developed a soft sensor based on an autoencoder neural network architecture.
Applied condition assessment methods to analyze sensor data and compressor performance.

Main Results:

The developed soft sensor successfully monitored the compressor's condition over a five-year period.
Significant changes and performance drift in the compressor were identified over time.
The study generated a qualitative indicator reflecting the long-term performance of the compressor.

Conclusions:

The autoencoder-based soft sensor is an effective tool for monitoring industrial gas turbine compressor condition.
Long-term monitoring reveals critical performance changes and degradation trends in compressors.
The developed qualitative indicator aids in understanding and managing compressor behavior for improved maintenance strategies.