Construction of Data-Driven Performance Digital Twin for a Real-World Gas Turbine Anomaly Detection Considering Uncertainty

Abstract

Anomaly detection and failure prediction of gas turbines is of great importance for ensuring reliable operation. This work presents a novel approach for anomaly detection based on a data-driven performance digital twin of gas turbine engines. The developed digital twin consists of two parts: uncertain performance digital twin (UPDT) and fault detection capability. UPDT is a probabilistic digital representation of the expected performance behavior of real-world gas turbine engines operating under various conditions. Fault detection capability is developed based on detecting UPDT outputs that have low probability under the training distribution. A novel anomaly measure based on the first Wasserstein distance is proposed to characterize the entire flight data, and a threshold can be applied to this measure to detect anomaly flights. The proposed UPDT with uncertainty quantification is trained with the sensor data from an individual physical reality and the outcome of the UPDT is intended to deliver the health assessment and fault detection results to support operation and maintenance decision-making. The proposed method is demonstrated on a real-world dataset from a typical type of commercial turbofan engine and the result shows that the F1 score reaches a maximum of 0.99 with a threshold of 0.45. The case study demonstrated that the proposed novel anomaly detection method can effectively identify the abnormal samples, and it is also possible to isolate anomalous behavior in a single performance signal, which is helpful for further fault diagnosis once an anomaly is detected.

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

“…Ma et al 2023 [14] presented a data-driven performance DT of GaT-AE for FD. The Uncertain Performance-DT (UPDT) represented the real-world GaT operation and the FD Capability (FDC) developed with the UPDT outcomes for the FD.…”

Digital twins for internal combustion engines: A brief review

“…Digital twins can detect the need for prompt maintenance by analyzing data patterns and departures from expected behavior. This proactive strategy enables maintenance employees to address emergent issues as they arise, avoiding costly problems and unanticipated downtime (Aghazadeh Ardebili et al, 2023) (Ma et al, 2023).…”

“…Rather than replacing every component, digital twins can identify individual components that require repair or substitution due to wear, stress, or breakage. This targeted strategy saves not only money but also important resources by reducing unnecessary exchanges (Li et al, 2022) (Ma et al, 2023).…”