Session: GT9.1 - Analytics & Digital Solutions and GT6.2 - Emerging Technologies (includes Wind Energy)

Paper Number: 161778

161778 - Gas Turbine Thermal Digital Twin: Developing a Data Engineering Framework 

A gas turbine engine (GT) is a complex power-producing machine that operates under high speed, pressure and temperature. Monitoring its health in real time is essential for ensuring safety and reliability. However harsh operating conditions, limited accessibility, and high instrumentation cost, make it challenging to get sensor data from all life limited locations. This can be addressed by developing and deploying a Digital Twin (DT) which utilizes engine data from test, fleet operations and numerical analysis, to calculate local and bulk metal temperatures at locations of interest.  

Development of this Digital Twin needs a specialized framework for data collection, pre-processing, model training, and evaluation. This paper focuses on data acquisition, transformation and engineering steps that are performed prior to the development of a DT, which are critical for enhancing its accuracy and robustness.

Data acquisition involves fetching real-time sensor data from in-house cloud system. Data transformation involves converting this data into a format readable by Python. The dataset includes a wide range of operating conditions that a GT experiences throughout its lifecycle, providing an exhaustive dataset for training the DT model. Data engineering involves removal of false sensor readings, filtration of dataset, data imputation for missing data, and noise reduction to deal with occasional spikes and noise, normalization of input features and target(s). These operations have been performed using python in-built standard libraries like Pandas, NumPy, scikit-learn. In addition, statistical techniques like Pearson’s correlation and Principal Component Analysis (PCA) are utilized in the feature engineering process.

The paper is intended to illustrate the process to develop a data engineering framework to develop GT Digital Twin.

Presenting Author: Vipin Pal Siemens Limited

Presenting Author Biography: With 4 years of experience in mechanical design, analysis, and integration of aero/industrial gas turbine components, I have extensive knowledge in propulsion systems and specialize in mechanical integrity and analysis of turbomachinery.