Gas turbine aero engine fault detection using Geo-TLSVM and digital twin with multimodal data analysis

Abstract

DT is the digital representation of physical systems to simulate real situations in a digital version. Here, the DT of the GaT-AE is created for the Fault Detection (FD) process. The GaT is a type of internal combustion engine that uses the air as the working fluid to propel the flight. However, the incorporation of big data analysis in the DT model is challenging in previous works. So, a multimodal GaT-AE fault identification system is proposed. First, the 3D print of the GaT-AE is created. Then, the data is collected from both built-in sensors and DT. Afterward, the multimodal data is pre-processed and balanced by using HT-QNN and QADASYN, respectively. Next, the features are processed by GT-PFS, and finally, the faults are detected by Geo-TLSVM. The analysis proved that the developed model outperformed the other state-of-the-art model with its effective FD rate of 97.24%.