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

Abstract

Maintenance is the process of preserving the good condition of a system to ensure its reliability and availability to perform specific operations. The way maintenance is nowadays performed in industry is changing thanks to the increasing availability of data and condition assessment methods. Soft sensors have been widely used over last years to monitor industrial processes and to predict process variables that are difficult to measured. The main objective of this study is to monitor and evaluate the condition of the compressor in a particular industrial gas turbine by developing a soft sensor following an autoencoder architecture. The data used to monitor and analyze its condition were captured by several sensors located along the compressor for around five years. The condition assessment of an industrial gas turbine compressor reveals significant changes over time, as well as a drift in its performance. These results lead to a qualitative indicator of the compressor behavior in long-term performance.

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

“…The results presented in this study are consistent with previous outcomes in the same gas turbines. In [ 21 ], we presented a soft sensor that is able to detect drift in the compressor data. This research went a step further by determining the operational change after major maintenance.…”

Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

“…During the last few years, condition monitoring techniques have evolved from visual inspections and manual analysis to more advanced techniques. Thanks to sensors, the most relevant health parameters of gas turbines are constantly being captured, and thereby, on the basis of these data, data analytics methods can treat more sophisticated systems, as well as handle uncertainties due to the stochastic degradation process [42]. The methods proposed in this section for condition monitoring are those approaches that are related to determining the condition of the GT or any of its components, i.e., the health status.…”