A novel digital twin approach based on deep multimodal information fusion for aero-engine fault diagnosis

Condition monitoring and fault diagnosis play an important role in the safety and reliability of aero-engine. Digital twin (DT) technology, which can realize the fusion of physical space and virtual space, has significant advantages over previous researches that only focus on physical mechanisms or big data. In this paper, a novel DT approach based on deep multimodal information fusion (MIF) is proposed, which integrates information from the physical-based model (PBM) and the data-driven model. Two deep Boltzmann machines (DBMs) are con-structed for feature extraction from sensor data and nonlinear component-level model simulation data, respec-tively. Whereby information from these two modalities is mapped into a high-dimensional space and forms a joint representation, and then combined with a multi-layer feedforward neural network to form the MIF model for real-time fault detection and isolation. In addition, an adaptive correction model for performance degradation is constructed by additionally analyzing the probability distribution of engine operation data. Compared with the traditional single-modality method, the proposed DT approach fuses the information of two key modalities and realizes the adaptive updating of the PBM model. The experimental results indicate that the proposed DT approach improves the accuracy of fault diagnosis and reduces the error of parameter prediction.

Automated summary

In this paper, a novel digital twin approach based on deep multimodal information fusion is proposed, which integrates information from physical-based models and data-driven models for real-time fault detection and isolation. The experimental results show that this approach improves the accuracy of fault diagnosis and reduces the error of parameter prediction.