# Multi-Source Information Fusion Diagnosis Method for Aero Engine
## 1 Introduction  
Aero engine operating conditions are harsh, with complex structures, high model nonlinearity, and high failure rates. Its function will inevitably deteriorate throughout its useful life cycle, which consequently causes anomalies, unexpected malfunctions, degradation, and faults, which seriously threaten the reliability and operational safety of the engine [1, 2, 3]. Recent advances in multi-sensor fusion technology have significantly contributed to the development of fault diagnosis methodologies. Compared with a single source of information, utilizing multi-source information fusion technology for aero engine fault diagnosis can more accurately assess the health status of the engine and improve diagnostic efficiency [4]. In the past decades, various fault diagnosis methods have been developed extensively, which can be generally divided into model-based methods and data-driven methods [5]. Model-based methods rely on detailed physics-based mathematical models to characterize engine behavior. Gas path analysis (GPA) [6] and Kalman filtering (KF) [7] are the two most representative model-based methods. [PERSON] et al. [8] proposed the concept of distributed-flame tube health state parameters and the Kalman filtering technique for engine combustion chamber fault detection and health state monitoring. [PERSON] et al. [9] developed a novel distributed extended Kalman filter with a data buffer bank (DEKF) and combined the DEKF with a self-tuning buffer strategy of recursive fusion estimation to form the self-tuning DEKF (SDEKF) algorithm for improving state estimation performance. With the advancement of sensor technologies and intelligent algorithms, data-driven methods have attracted increasing attention, such as artificial neural networks (ANNs), support vector machines (SVMs), and deep learning, which do not require detailed mechanical knowledge of the engine [10; 11; 12]. [PERSON] et al. [13] proposed a multivariate health estimation model and a multivariate multistep-ahead long-term degradation prediction model to estimate the remaining useful life of aero engines. [PERSON] et al. [14] proposed a continuous domain distribution adversarial network based on a deep domain adversarial network. [PERSON] et al. [15] proposed a new compound fault diagnosis framework, the Lightweight Spatial-Temporal Model Fusion Self-Attention Mechanism (LST-SATM-Net), for aero engine hydraulic pipeline systems. [PERSON] et al. [16] proposed a dynamic threshold method for aero engine fault detection based on Isolation Forest, which only uses normal aero engine data for training to build the fault detection model. These methods rely on single-source fault information to estimate the engine's behavior and condition and are not comprehensive. As the volume of acquired information increases, single-source diagnostic methods face growing challenges in completing the inference process. Aero engine fault diagnosis is divided into gas path fault systems, vibration fault systems, and lubricating oil fault systems [17]. However, most current research studies generally conduct fault diagnosis using information from a single physical source. On the one hand, diagnosis based on single physical sources can be lopsided. On the other hand, as a way to protect information confidentiality and integrity, the operating data of an engine are impossible to share for model training. Information fusion technology can deal with complex multi-source information effectively and improve the accuracy and reliability of diagnosis. More and more attention has been paid to the field of fault diagnosis [18; 19]. [PERSON] et al. [20] proposed a deep multimodal fusion structure suitable for multi-source information, which provides a new solution for fusing real physical data and virtual simulation data in the aero engine digital twin scheme. The Bayesian network, due to its ability to integrate heterogeneous information sources, has been widely employed in multi-source fusion diagnosis. [PERSON] et al.