An enhanced digital twin-driven fault detection and isolation method based on sensor series imaging mechanism for gas turbine engine

Abstract

Condition based maintenance plays a crucial role in ensuring the safety and reliability of gas turbine engine. Specifically, fault detection and isolation (FDI) can improve efficiency and reduce costs during maintenance. The traditional FDI methods, which developed only using physical engine data, lack a comprehensive consideration of high accuracy and strong applicability across different engine individuals. In this paper, an enhanced digital twin-driven FDI method is proposed, which combines a digital engine, sensor series imaging mechanism and a convolutional neural network. The digital engine is constructed to mirror the operational status of the physical engine. The output residuals between the physical and digital engines are transformed into multi-channel images to train a convolutional neural network for FDI of the physical engine. The network is then fine-tuned using fault-free data from the target engine to further strengthen diagnostic performance across different engine individuals. The effectiveness of the digital engine is verified using actual ground test data, and the performance of the proposed method is demonstrated through comparative simulations. The network, trained on operational data from Engine A and fine-tuned with fault-free data from other engines, exhibits the highest diagnostic accuracy and fault isolation rate. Compared with the physical engine data-based method, the accuracy is increased from 68.25% to 97.95%, the fault isolation rate is improved from 85.91% to 99.26%, and the false alarm rate is decreased from 4.41% to 0.34%. The results show that the proposed method has better FDI capabilities across different engine individuals.